diff --git a/paperqa/agents/helpers.py b/paperqa/agents/helpers.py
index 8183a912..001f9d06 100644
--- a/paperqa/agents/helpers.py
+++ b/paperqa/agents/helpers.py
@@ -5,6 +5,7 @@
 from datetime import datetime
 from typing import cast
 
+from aviary.core import Message
 from llmclient import LiteLLMModel, LLMModel
 from rich.table import Table
 
@@ -60,12 +61,14 @@ async def litellm_get_search_query(
         )
     else:
         model = llm
-    result = await model.run_prompt(
-        prompt=search_prompt,
-        data={"question": question, "count": count},
-        system_prompt=None,
+    data = {"question": question, "count": count}
+    messages = [
+        Message(role="user", content=search_prompt.format(**data)),
+    ]
+    result = await model.call_single(  # run_prompt is deprecated
+        messages=messages,
     )
-    search_query = result.text
+    search_query = cast(str, result.text)
     queries = [s for s in search_query.split("\n") if len(s) > 3]  # noqa: PLR2004
     # remove "2.", "3.", etc. -- https://regex101.com/r/W2f7F1/1
     queries = [re.sub(r"^\d+\.\s*", "", q) for q in queries]
diff --git a/paperqa/agents/models.py b/paperqa/agents/models.py
index 4658a3fd..4e52caf4 100644
--- a/paperqa/agents/models.py
+++ b/paperqa/agents/models.py
@@ -5,9 +5,10 @@
 import time
 from contextlib import asynccontextmanager
 from enum import StrEnum
-from typing import Any, ClassVar, Protocol
+from typing import Any, ClassVar, Protocol, cast
 from uuid import UUID, uuid4
 
+from aviary.core import Message
 from llmclient import LiteLLMModel, LLMModel
 from pydantic import (
     BaseModel,
@@ -79,12 +80,16 @@ async def get_summary(self, llm_model: LLMModel | str = "gpt-4o") -> str:
         model = (
             LiteLLMModel(name=llm_model) if isinstance(llm_model, str) else llm_model
         )
-        result = await model.run_prompt(
-            prompt="{question}\n\n{answer}",
-            data={"question": self.session.question, "answer": self.session.answer},
-            system_prompt=sys_prompt,
+        data = {"question": self.session.question, "answer": self.session.answer}
+        prompt = "{question}\n\n{answer}"
+        messages = [
+            Message(role="system", content=sys_prompt),
+            Message(role="user", content=prompt.format(**data)),
+        ]
+        result = await model.call_single(  # run_prompt is deprecated
+            messages=messages,
         )
-        return result.text.strip()
+        return cast(str, result.text).strip()
 
 
 class TimerData(BaseModel):
diff --git a/paperqa/core.py b/paperqa/core.py
index 578858f2..b269f2fd 100644
--- a/paperqa/core.py
+++ b/paperqa/core.py
@@ -3,9 +3,11 @@
 import json
 import re
 from collections.abc import Callable, Sequence
-from typing import Any
+from typing import Any, cast
+
+from aviary.core import Message
+from llmclient import LLMModel
 
-from paperqa.llms import PromptRunner
 from paperqa.types import Context, LLMResult, Text
 from paperqa.utils import extract_score, strip_citations
 
@@ -38,7 +40,8 @@ def escape_newlines(match: re.Match) -> str:
 async def map_fxn_summary(
     text: Text,
     question: str,
-    prompt_runner: PromptRunner | None,
+    summary_llm_model: LLMModel | None,
+    prompt_details: tuple[str, str] | None,
     extra_prompt_data: dict[str, str] | None = None,
     parser: Callable[[str], dict[str, Any]] | None = None,
     callbacks: Sequence[Callable[[str], None]] | None = None,
@@ -51,12 +54,13 @@ async def map_fxn_summary(
 
     Args:
         text: The text to parse.
-        question: The question to use for the chain.
-        prompt_runner: The prompt runner to call - should have question, citation,
-            summary_length, and text fields.
-        extra_prompt_data: Optional extra kwargs to pass to the prompt runner's data.
-        parser: The parser to use for parsing - return empty dict on Failure to fallback to text parsing.
-        callbacks: LLM callbacks to execute in the prompt runner.
+        question: The question to use for summarization.
+        summary_llm_model: The LLM model to use for generating summaries.
+        prompt_details: Tuple containing the prompt template and system prompt.
+        extra_prompt_data: Optional extra data to pass to the prompt template.
+        parser: Optional parser function to parse LLM output into structured data.
+            Should return dict with at least 'summary' field.
+        callbacks: Optional sequence of callback functions to execute during LLM calls.
 
     Returns:
         The context object and LLMResult to get info about the LLM execution.
@@ -67,14 +71,25 @@ async def map_fxn_summary(
     citation = text.name + ": " + text.doc.formatted_citation
     success = False
 
-    if prompt_runner:
-        llm_result = await prompt_runner(
-            {"question": question, "citation": citation, "text": text.text}
-            | (extra_prompt_data or {}),
-            callbacks,
-            "evidence:" + text.name,
+    if summary_llm_model and prompt_details:
+        data = {
+            "question": question,
+            "citation": citation,
+            "text": text.text,
+            **(extra_prompt_data or {}),
+        }
+        message_prompt = prompt_details[0]
+        system_prompt = prompt_details[1]
+        messages = [
+            Message(role="system", content=system_prompt.format(**data)),
+            Message(role="user", content=message_prompt.format(**data)),
+        ]
+        llm_result = await summary_llm_model.call_single(  # run_prompt is deprecated
+            messages=messages,
+            callbacks=callbacks,
+            name="evidence:" + text.name,
         )
-        context = llm_result.text
+        context = cast(str, llm_result.text)
         result_data = parser(context) if parser else {}
         success = bool(result_data)
         if success:
diff --git a/paperqa/docs.py b/paperqa/docs.py
index 75e82f47..908d6929 100644
--- a/paperqa/docs.py
+++ b/paperqa/docs.py
@@ -8,12 +8,12 @@
 import urllib.request
 from collections.abc import Callable, Sequence
 from datetime import datetime
-from functools import partial
 from io import BytesIO
 from pathlib import Path
 from typing import Any, BinaryIO, cast
 from uuid import UUID, uuid4
 
+from aviary.core import Message
 from llmclient import (
     Embeddable,
     EmbeddingModel,
@@ -33,7 +33,6 @@
 from paperqa.core import llm_parse_json, map_fxn_summary
 from paperqa.llms import (
     NumpyVectorStore,
-    PromptRunner,
     VectorStore,
 )
 from paperqa.paths import PAPERQA_DIR
@@ -294,12 +293,16 @@ async def aadd(  # noqa: PLR0912
             )
             if not texts:
                 raise ValueError(f"Could not read document {path}. Is it empty?")
-            result = await llm_model.run_prompt(
-                prompt=parse_config.citation_prompt,
-                data={"text": texts[0].text},
-                system_prompt=None,  # skip system because it's too hesitant to answer
+            data = {"text": texts[0].text}
+            messages = [
+                Message(
+                    role="user", content=parse_config.citation_prompt.format(**data)
+                ),
+            ]
+            result = await llm_model.call_single(  # run_prompt is deprecated
+                messages=messages,
             )
-            citation = result.text
+            citation = cast(str, result.text)
             if (
                 len(citation) < 3  # noqa: PLR2004
                 or "Unknown" in citation
@@ -315,10 +318,15 @@ async def aadd(  # noqa: PLR0912
         # try to extract DOI / title from the citation
         if (doi is title is None) and parse_config.use_doc_details:
             # TODO: specify a JSON schema here when many LLM providers support this
-            result = await llm_model.run_prompt(
-                prompt=parse_config.structured_citation_prompt,
-                data={"citation": citation},
-                system_prompt=None,
+            data = {"citation": citation}
+            messages = [
+                Message(
+                    role="user",
+                    content=parse_config.structured_citation_prompt.format(**data),
+                ),
+            ]
+            result = await llm_model.call_single(  # run_prompt is deprecated
+                messages=messages,
             )
             # This code below tries to isolate the JSON
             # based on observed messages from LLMs
@@ -326,7 +334,7 @@ async def aadd(  # noqa: PLR0912
             # the first { and last } in the response.
             # Since the anticipated structure should  not be nested,
             # we don't have to worry about nested curlies.
-            clean_text = result.text.split("{", 1)[-1].split("}", 1)[0]
+            clean_text = cast(str, result.text).split("{", 1)[-1].split("}", 1)[0]
             clean_text = "{" + clean_text + "}"
             try:
                 citation_json = json.loads(clean_text)
@@ -609,19 +617,17 @@ async def aget_evidence(
             else matches
         )
 
-        prompt_runner: PromptRunner | None = None
+        prompt_details = None
         if not answer_config.evidence_skip_summary:
             if prompt_config.use_json:
-                prompt_runner = partial(
-                    summary_llm_model.run_prompt,
+                prompt_details = (
                     prompt_config.summary_json,
-                    system_prompt=prompt_config.summary_json_system,
+                    prompt_config.summary_json_system,
                 )
             else:
-                prompt_runner = partial(
-                    summary_llm_model.run_prompt,
+                prompt_details = (
                     prompt_config.summary,
-                    system_prompt=prompt_config.system,
+                    prompt_config.system,
                 )
 
         with set_llm_session_ids(session.id):
@@ -631,7 +637,8 @@ async def aget_evidence(
                     map_fxn_summary(
                         text=m,
                         question=session.question,
-                        prompt_runner=prompt_runner,
+                        summary_llm_model=summary_llm_model,
+                        prompt_details=prompt_details,
                         extra_prompt_data={
                             "summary_length": answer_config.evidence_summary_length,
                             "citation": f"{m.name}: {m.doc.formatted_citation}",
@@ -712,12 +719,15 @@ async def aquery(  # noqa: PLR0912
         pre_str = None
         if prompt_config.pre is not None:
             with set_llm_session_ids(session.id):
-                pre = await llm_model.run_prompt(
-                    prompt=prompt_config.pre,
-                    data={"question": session.question},
+                data = {"question": session.question}
+                messages = [
+                    Message(role="system", content=prompt_config.system),
+                    Message(role="user", content=prompt_config.pre.format(**data)),
+                ]
+                pre = await llm_model.call_single(  # run_prompt is deprecated
+                    messages=messages,
                     callbacks=callbacks,
                     name="pre",
-                    system_prompt=prompt_config.system,
                 )
             session.add_tokens(pre)
             pre_str = pre.text
@@ -766,19 +776,22 @@ async def aquery(  # noqa: PLR0912
             )
         else:
             with set_llm_session_ids(session.id):
-                answer_result = await llm_model.run_prompt(
-                    prompt=prompt_config.qa,
-                    data={
-                        "context": context_str,
-                        "answer_length": answer_config.answer_length,
-                        "question": session.question,
-                        "example_citation": prompt_config.EXAMPLE_CITATION,
-                    },
+                data = {
+                    "context": context_str,
+                    "answer_length": answer_config.answer_length,
+                    "question": session.question,
+                    "example_citation": prompt_config.EXAMPLE_CITATION,
+                }
+                messages = [
+                    Message(role="system", content=prompt_config.system),
+                    Message(role="user", content=prompt_config.qa.format(**data)),
+                ]
+                answer_result = await llm_model.call_single(  # run_prompt is deprecated
+                    messages=messages,
                     callbacks=callbacks,
                     name="answer",
-                    system_prompt=prompt_config.system,
                 )
-            answer_text = answer_result.text
+            answer_text = cast(str, answer_result.text)
             session.add_tokens(answer_result)
         # it still happens
         if (ex_citation := prompt_config.EXAMPLE_CITATION) in answer_text:
@@ -806,14 +819,17 @@ async def aquery(  # noqa: PLR0912
 
         if prompt_config.post is not None:
             with set_llm_session_ids(session.id):
-                post = await llm_model.run_prompt(
-                    prompt=prompt_config.post,
-                    data=session.model_dump(),
+                data = {"question": session.question}
+                messages = [
+                    Message(role="system", content=prompt_config.system),
+                    Message(role="user", content=prompt_config.post.format(**data)),
+                ]
+                post = await llm_model.call_single(  # is deprecated
+                    messages=messages,
                     callbacks=callbacks,
                     name="post",
-                    system_prompt=prompt_config.system,
                 )
-            answer_text = post.text
+            answer_text = cast(str, post.text)
             session.add_tokens(post)
             formatted_answer = f"Question: {session.question}\n\n{post}\n"
             if bib:
diff --git a/paperqa/llms.py b/paperqa/llms.py
index e797e337..2d403720 100644
--- a/paperqa/llms.py
+++ b/paperqa/llms.py
@@ -5,7 +5,6 @@
 import uuid
 from abc import ABC, abstractmethod
 from collections.abc import (
-    Awaitable,
     Callable,
     Iterable,
     Sequence,
@@ -20,7 +19,6 @@
     EmbeddingModes,
     HybridEmbeddingModel,
     LiteLLMEmbeddingModel,
-    LLMResult,
     SentenceTransformerEmbeddingModel,
     SparseEmbeddingModel,
 )
@@ -46,11 +44,6 @@
 except ImportError:
     qdrant_installed = False
 
-PromptRunner = Callable[
-    [dict, Sequence[Callable[[str], None]] | None, str | None],
-    Awaitable[LLMResult],
-]
-
 logger = logging.getLogger(__name__)
 
 
diff --git a/tests/cassettes/test_pdf_reader_match_doc_details.yaml b/tests/cassettes/test_pdf_reader_match_doc_details.yaml
index 24df5d86..9cee1e56 100644
--- a/tests/cassettes/test_pdf_reader_match_doc_details.yaml
+++ b/tests/cassettes/test_pdf_reader_match_doc_details.yaml
@@ -6,7 +6,7 @@ interactions:
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         list of authors. Wellawatte et al, A Perspective on Explanations of Molecular
         Prediction Models, XAI Review, 2023\n\nCitation JSON:"}], "model": "gpt-4o-2024-11-20",
-        "temperature": 0.0}'
+        "n": 1, "temperature": 0.0}'
       headers:
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         \"...\"\n}\n\nwhere `summary` is relevant information from the text - about
         100 words words. `relevance_score` is an integer 1-10 for the relevance of `summary`
         to the question.\n"}, {"role": "user", "content": "Excerpt from wellawatteUnknownyearaperspectiveon
-        pages 20-22: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        pages 12-14: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
         D. White. A perspective on explanations of molecular prediction models. Journal
         of Chemical Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
         doi:10.1021/acs.jctc.2c01235. This article has 23 citations and is from a domain
-        leading peer-reviewed journal.\n\n----\n\necule.  The counterfactual indicates\nstructural
-        changes to ethyl benzoate that would result in the model predicting the molecule\nto
-        not contain the \u2018fruity\u2019 scent. The Tanimoto96 similarity between
-        the counterfactual and\n2,4 decadienal is also provided. Republished with permission
-        from authors.31\n\n\n   The molecule 2,4-decadienal, which is known to have
-        a \u2018fatty\u2019 scent, is analyzed in Fig-\n\nure 5.142,143 The resulting
-        counterfactual, which has a shorter carbon chain and no carbonyl\n\ngroups,
-        highlights the influence of these structural features on the \u2018fatty\u2019
-        scent of 2,4 deca-\n\ndienal. To generalize to other molecules, Seshadri et
-        al. 31 applied the descriptor attribution\n\nmethod to obtain global explanations
-        for the scents. The global explanation for the \u2018fatty\u2019\n\nscent was
-        generated by gathering chemical spaces around many \u2018fatty\u2019 scented
-        molecules.\n\nThe resulting natural language explanation is: \u201cThe molecular
-        property \u201cfatty scent\u201d can\n\nbe explained by the presence of a heptanyl
-        fragment, two CH2 groups separated by four\n\n\n                                       20bonds,
-        and a C=O double bond, as well as the lack of more than one or two O atoms.\u201d31\n\nThe
-        importance of a heptanyl fragment aligns with that reported in the literature,
-        as \u2018fatty\u2019\n\nmolecules often have a long carbon chain.144 Furthermore,
-        the importance of a C=O dou-\n\nble bond is supported by the findings reported
-        by Licon et al. 145, where in addition to a\n\n\u201clarger carbon-chain skeleton\u201d,
-        they found that \u2018fatty\u2019 molecules also had \u201caldehyde or acid\n\nfunctions\u201d.145
-        For the \u2018pineapple\u2019 scent, the following natural language explanation
-        was ob-\n\ntained: \u201cThe molecular property \u201cpineapple scent\u201d
-        can be explained by the presence of ester,\n\nethyl/ether O group, alkene/ether
-        O group, and C=O double bond, as well as the absence of\n\nan Aromatic atom.\u201d31
-        Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\n\nple volatile
-        compounds.146,147 The combination of a C=O double bond with an ether could\n\nalso
-        correspond to an ester group. Additionally, aldehydes and ketones, which contain
-        C=O\n\ndouble bonds, are also common in pineapple volatile compounds.146,148\n\n\nDiscussion\n\n\nWe
-        have shown two post-hoc XAI applications based on molecular counterfactual expla-\n\nnations9
-        and descriptor explanations.10 These methods can be used to explain black-box\n\nmodels
-        whose input is a molecule. These two methods can be applied for both classification\n\nand
-        regression tasks. Note that the \u201ccorrectness\u201d of the explanations
-        strongly depends on\n\nthe accuracy of the black-box model.\n\n  A molecular
-        counterfactual is one with a minimal distance from a base molecular, but\n\nwith
-        contrasting chemical properties.  In the above examples, we used Tanimoto similar-\n\nity96
-        of ECFP4 fingreprints97 as distance, although this should be explored in the
-        future.\n\nCounterfactual explanations are useful because they are represented
-        as chemical structures\n\n(familiar to domain experts), sparse, and are actionable.
-        A few other popular examples of\n\ncounterfactual on graph methods are GNNExplainer,
-        MEG and CF-GNNExplainer.69,104,105\n\n   The descriptor explanation method developed
-        by Gandhi and White 10 fits a self-explaining\n\n\n\n                                       21surrogate
-        model to explain the black-box model. This is similar to the GraphLIME87 method,\n\nalthough
-        we have the flexibility to use explanation features other than subgraphs. Futher-\n\nmore,
-        we show that natural language combined with chemical descriptor attributions
-        can\n\ncreate explanations useful for chemists, thus enhancing the accessibility
-        of DL in chemistry.\n\nLastly, we examined if XAI can be used beyond interpretation.
-        Work by Seshadri et al. 31 use\n\nMMACE and surrogate model explanations to
-        analyze the structure-property relationships\n\nof scent. They recovered known
-        structure-property relationships for molecular scent purely\n\nfrom explanations,
-        demonstrating the usefulness of a two step process: fit an accurate model\n\nand
-        then explain it.\n\n   Choosing among the plethora of XAI methods described
-        here is still an open question.\n\nIt remains to be seen if there will ever
-        be a consensus benchmark, since this field sits on\n\nthe intersection of human-machine
-        interaction, machine learning, and philosophy (i.e., what\n\nconstitutes an
-        explanation?). Our current advice is to consider first the audience \u2013 domain\n\nexperts
-        or ML experts or non-experts \u2013 and what the explanations should accomplish.
-        Are\n\nthey meant to inform data selection or model building, how a prediction
-        is used, or how the\n\nfeatures can be changed to affect the outcome. The second
-        consideration is what access you\n\nhave to the underlying model. The ability
-        to have model derivatives or propagate gradients\n\nto the input to models informs
-        the XAI method.\n\n\nConclusion and outlook\n\n\nWe should seek to explain molecular
-        property prediction models because users are more\n\nlikely to trust explained
-        predictions, and explanations can help assess if the model is learning\n\nthe
-        corr\n\n----\n\nQuestion: Are counterfactuals actionable? [yes/no]\n\n"}], "model":
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+        leading peer-reviewed journal.\n\n----\n\ntual approach, contrastive approach
+        employ a dual\n\noptimization method, which works by generating a similar and
+        a dissimilar (counterfactuals)\n\nexample. Contrastive explanations can interpret
+        the model by identifying contribution of\n\npresence and absence of subsets
+        of features towards a certain prediction.36,99\n\n  A counterfactual x\u2032
+        of an instance x is one with a dissimilar prediction \u02c6f(x) in classi-\n\nfication
+        tasks. As shown in equation 5, counterfactual generation can be thought of as
+        a\n\nconstrained optimization problem which minimizes the vector distance d(x,
+        x\u2032) between the\n\nfeatures.9,100\n\n\n                              minimize  d(x,
+        x\u2032)\n                                                                                           (5)\n                               such
+        that   \u02c6f(x) \u0338= \u02c6f(x\u2032)\n\n   For regression tasks, equation
+        6 adapted from equation 5 can be used. Here, a counter-\n\nfactual is one with
+        a defined increase or decrease in the prediction.\n\n\n                           minimize  d(x,
+        x\u2032)\n                                                                                           (6)\n                            such
+        that    \u02c6f(x) \u2212\u02c6f(x\u2032) \u2265\u2206\n\n   Counterfactuals
+        explanations have become a useful tool for XAI in chemistry, as they\n\nprovide
+        intuitive understanding of predictions and are able to uncover spurious relationships\n\nin
+        training data.101 Counterfactuals create local (instance-level), actionable
+        explanations.\n\nActionability of an explanation suggest which features can
+        be altered to change the outcome.\n\nFor example, changing a hydrophobic functional
+        group in a molecule to a hydrophilic group\n\nto increase solubility.\n\n   Counterfactual
+        generation is a demanding task as it requires gradient optimization over\n\ndiscrete
+        features that represents a molecule. Recent work by Fu et al. 102 and Shen et
+        al. 103\n\npresent two techniques which allow continuous gradient-based optimization.
+        Although, these\n\nmethodologies are shown to circumvent the issue of discrete
+        molecular optimization, counter-\n\nfactual explanation based model interpretation
+        still remains unexplored compared to other\n\n\n\n                                       12post-hoc
+        methods.\n\n   CF-GNNExplainer104 is a counterfactual explanation generating
+        method based on GN-\n\nNExplainer69 for graph data. This method generate counterfactuals
+        by perturbing the input\n\ndata (removing edges in the graph), and keeping account
+        of perturbations which lead to\n\nchanges in the output.  However, this method
+        is only applicable to graph-based models\n\nand can generate infeasible molecular
+        structures. Another related work by Numeroso and\n\nBacciu 105 focus on generating
+        counterfactual explanations for deep graph networks. Their\n\nmethod MEG (Molecular
+        counterfactual Explanation Generator) uses a reinforcement learn-\n\ning based
+        generator to create molecular counterfactuals (molecular graphs).  While this\n\nmethod
+        is able to generate counterfactuals through a multi-objective reinforcement
+        learner,\n\nthis is not a universal approach and requires training the generator
+        for each task.\n\n   Work by Wellawatte et al. 9 present a model agnostic counterfactual
+        generator MMACE\n\n(Molecular Model Agnostic Counterfactual Explanations) which
+        does not require training\n\nor computing gradients. This method firstly populates
+        a local chemical space through ran-\n\ndom string mutations of SELFIES106 molecular
+        representations using the STONED algo-\n\nrithm.107 Next, the labels (predictions)
+        of the molecules in the local space are generated\n\nusing the model that needs
+        to be explained. Finally, the counterfactuals are identified and\n\nsorted by
+        their similarities \u2013 Tanimoto distance96 between ECFP4 fingerprints.97
+        Unlike the\n\nCF-GNNExplainer104 and MEG105 methods, the MMACE algorithm ensures
+        that generated\n\nmolecules are valid, owing to the surjective property of SELFIES.
+        Additionally, the MMACE\n\nmethod can be applied to both regression and classification
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-        pages 12-14: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        pages 20-22: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
         D. White. A perspective on explanations of molecular prediction models. Journal
         of Chemical Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
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+        changes to ethyl benzoate that would result in the model predicting the molecule\nto
+        not contain the \u2018fruity\u2019 scent. The Tanimoto96 similarity between
+        the counterfactual and\n2,4 decadienal is also provided. Republished with permission
+        from authors.31\n\n\n   The molecule 2,4-decadienal, which is known to have
+        a \u2018fatty\u2019 scent, is analyzed in Fig-\n\nure 5.142,143 The resulting
+        counterfactual, which has a shorter carbon chain and no carbonyl\n\ngroups,
+        highlights the influence of these structural features on the \u2018fatty\u2019
+        scent of 2,4 deca-\n\ndienal. To generalize to other molecules, Seshadri et
+        al. 31 applied the descriptor attribution\n\nmethod to obtain global explanations
+        for the scents. The global explanation for the \u2018fatty\u2019\n\nscent was
+        generated by gathering chemical spaces around many \u2018fatty\u2019 scented
+        molecules.\n\nThe resulting natural language explanation is: \u201cThe molecular
+        property \u201cfatty scent\u201d can\n\nbe explained by the presence of a heptanyl
+        fragment, two CH2 groups separated by four\n\n\n                                       20bonds,
+        and a C=O double bond, as well as the lack of more than one or two O atoms.\u201d31\n\nThe
+        importance of a heptanyl fragment aligns with that reported in the literature,
+        as \u2018fatty\u2019\n\nmolecules often have a long carbon chain.144 Furthermore,
+        the importance of a C=O dou-\n\nble bond is supported by the findings reported
+        by Licon et al. 145, where in addition to a\n\n\u201clarger carbon-chain skeleton\u201d,
+        they found that \u2018fatty\u2019 molecules also had \u201caldehyde or acid\n\nfunctions\u201d.145
+        For the \u2018pineapple\u2019 scent, the following natural language explanation
+        was ob-\n\ntained: \u201cThe molecular property \u201cpineapple scent\u201d
+        can be explained by the presence of ester,\n\nethyl/ether O group, alkene/ether
+        O group, and C=O double bond, as well as the absence of\n\nan Aromatic atom.\u201d31
+        Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\n\nple volatile
+        compounds.146,147 The combination of a C=O double bond with an ether could\n\nalso
+        correspond to an ester group. Additionally, aldehydes and ketones, which contain
+        C=O\n\ndouble bonds, are also common in pineapple volatile compounds.146,148\n\n\nDiscussion\n\n\nWe
+        have shown two post-hoc XAI applications based on molecular counterfactual expla-\n\nnations9
+        and descriptor explanations.10 These methods can be used to explain black-box\n\nmodels
+        whose input is a molecule. These two methods can be applied for both classification\n\nand
+        regression tasks. Note that the \u201ccorrectness\u201d of the explanations
+        strongly depends on\n\nthe accuracy of the black-box model.\n\n  A molecular
+        counterfactual is one with a minimal distance from a base molecular, but\n\nwith
+        contrasting chemical properties.  In the above examples, we used Tanimoto similar-\n\nity96
+        of ECFP4 fingreprints97 as distance, although this should be explored in the
+        future.\n\nCounterfactual explanations are useful because they are represented
+        as chemical structures\n\n(familiar to domain experts), sparse, and are actionable.
+        A few other popular examples of\n\ncounterfactual on graph methods are GNNExplainer,
+        MEG and CF-GNNExplainer.69,104,105\n\n   The descriptor explanation method developed
+        by Gandhi and White 10 fits a self-explaining\n\n\n\n                                       21surrogate
+        model to explain the black-box model. This is similar to the GraphLIME87 method,\n\nalthough
+        we have the flexibility to use explanation features other than subgraphs. Futher-\n\nmore,
+        we show that natural language combined with chemical descriptor attributions
+        can\n\ncreate explanations useful for chemists, thus enhancing the accessibility
+        of DL in chemistry.\n\nLastly, we examined if XAI can be used beyond interpretation.
+        Work by Seshadri et al. 31 use\n\nMMACE and surrogate model explanations to
+        analyze the structure-property relationships\n\nof scent. They recovered known
+        structure-property relationships for molecular scent purely\n\nfrom explanations,
+        demonstrating the usefulness of a two step process: fit an accurate model\n\nand
+        then explain it.\n\n   Choosing among the plethora of XAI methods described
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+        interaction, machine learning, and philosophy (i.e., what\n\nconstitutes an
+        explanation?). Our current advice is to consider first the audience \u2013 domain\n\nexperts
+        or ML experts or non-experts \u2013 and what the explanations should accomplish.
+        Are\n\nthey meant to inform data selection or model building, how a prediction
+        is used, or how the\n\nfeatures can be changed to affect the outcome. The second
+        consideration is what access you\n\nhave to the underlying model. The ability
+        to have model derivatives or propagate gradients\n\nto the input to models informs
+        the XAI method.\n\n\nConclusion and outlook\n\n\nWe should seek to explain molecular
+        property prediction models because users are more\n\nlikely to trust explained
+        predictions, and explanations can help assess if the model is learning\n\nthe
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-        A.; Pasculli, G.; Feldmann, C.; Rodr\u00b4\u0131guez-P\u00b4erez, R.; Bajorath,
-        J. Edge-\n\n    SHAPer: Bond-Centric Shapley Value-Based Explanation Method
-        for Graph Neural\n\n     Networks. iScience 2022, 25, 105043.\n\n\n(79) White,
-        A. D. Deep learning for molecules and materials. Living Journal of Computa-\n\n      tional
-        Molecular Science 2022, 3.\n\n(80) \u02d8Strumbelj, E.; Kononenko, I. Explaining
-        prediction models and individual predictions\n\n     with feature contributions.
-        Knowledge and Information Systems 2014, 41, 647\u2013665.\n\n\n(81) Erhan, D.;
-        Bengio, Y.; Courville, A.; Vincent, P. Visualizing Higher-Layer Features of\n\n     a
-        Deep Network. Technical Report, Univerist\u00b4e de Montr\u00b4eal 2009,\n\n\n(82)
-        Weber, J. K.; Morrone, J. A.; Bagchi, S.; Pabon, J. D.; gu Kang, S.; Zhang,
-        L.;\n\n     Cornell, W. D. Simplified, interpretable graph convolutional neural
-        networks for small\n\n     molecule activity prediction. Journal of Computer-Aided
-        Molecular Design 2022, 36,\n\n     391\u2013404.\n\n\n(83) Riniker, S.; Landrum,
-        G. A. Similarity maps - A visualization strategy for molecular\n\n     fingerprints
-        and machine-learning methods. Journal of Cheminformatics 2013, 5, 1\u20137.\n\n\n(84)
-        Humer, C.; Heberle, H.; Montanari, F.; Wolf, T.; Huber, F.; Henderson, R.; Hein-\n\n      rich,
-        J.; Streit, M. ChemInformatics Model Explorer (CIME): exploratory analysis of\n\n     chemical
-        model explanations. Journal of Cheminformatics 2022, 14, 1\u201314.\n\n\n(85)
-        McGrath, T.; Kapishnikov, A.; Toma\u02c7sev, N.; Pearce, A.; Wattenberg, M.;
-        Hass-\n\n      abis, D.; Kim, B.; Paquet, U.; Kramnik, V. Acquisition of chess
-        knowledge in Al-\n\n     phaZero. Proceedings of the National Academy of Sciences
-        2022, 119, e2206625119.\n\n\n\n\n                                      33(86)
-        Bajusz, D.; R\u00b4acz, A.; H\u00b4eberger, K. Why is Tanimoto index an appropriate
-        choice for\n\n     fingerprint-based similarity calculations? Journal of Cheminformatics
-        2015, 7, 1\u201313.\n\n\n(87) Huang, Q.; Yamada, M.; Tian, Y.; Singh, D.; Yin,
-        D.; Chang, Y. GraphLIME:\n\n     Local Interpretable Model Explanations for
-        Graph Neural Networks. CoRR 2020,\n\n     abs/2001.06216.\n\n\n(88) Sokol, K.;
-        Flach, P. A. LIMEtree: Interactively Customisable Explanations Based on\n\n     Local
-        Surrogate Multi-output Regression Trees. CoRR 2020, abs/2005.01427.\n\n\n(89)
-        Whitmore, L. S.; George, A.; Hudson, C. M. Mapping chemical performance on molec-\n\n     ular
-        structures using locally interpretable explanations. 2016; https://arxiv.org/\n\n    abs/1611.07443.\n\n\n(90)
-        Mehdi, S.; Tiwary, P. Thermodynamics of Interpretation. 2022,\n\n\n(91) H\u00a8ofler,
-        M. Causal inference based on counterfactuals. BMC Medical Research Method-\n\n     ology
-        2005, 5, 1\u201312.\n\n\n(92) Woodward, J.; Hitchcock, C. Explanatory Generalizations,
-        Part I: A Counterfactual\n\n     Account. No\u02c6us 2003, 37, 1\u201324.\n\n\n(93)
-        Frisch, M. F. Theories, models, and explanation; University of California, Berkeley,\n\n     1998.\n\n\n(94)
-        Reutlinger, A. Is There A Monist Theory of Causal and Non-Causal Explanations?\n\n    The
-        Counterfactual Theory of Scientific Explanation. Philosophy of Science 2016,
-        83,\n\n     733\u2013745.\n\n\n(95) Lewis, D. Causation. The journal of philosophy
-        1974, 70, 556\u2013567.\n\n\n(96) Tanimoto, T. T. Elementary mathematical theory
-        of classification and prediction.\n\n     Internal IBM Technical Report 1958,\n\n\n                                      34
-        (97) Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. Journal of Chemical
-        In-\n\n      formation and Modeling 2010, 50, 742\u2013754, PMID: 20426451.\n\n\n
-        (98) Mohapatra, S.; An, J.; G\u00b4omez-Bombarelli, R. Chemistry-informed macromolecule\n\n      graph
-        representation for similarity computation, unsupervised and supervised learn-\n\n       ing.
-        Machine Learning: Science and Technology 2022, 3, 015028.\n\n\n (99) Doshi-Velez,
-        F.; Kortz, M.; Budish, R.; Bavitz, C.; Gershman, S.; O\u2019Brien, D.;\n\n       Scott,
-        K.; Schieber, S.; Waldo, J.; Weinberger, D.; Weller, A.; Wood, A. Account-\n\n       ability
-        of AI Under the Law: The Role of Explanation. SSRN Electronic Journal\n\n     2017,\n\n\n(100)
-        Wachter, S.; Mittelstadt, B.; Russell, C. Counterfactual explanations without
-        opening\n\n      the black box: Automated decisions and the GDPR. Harv. JL &
-        Tech. 2017, 31, 841.\n\n\n(101) Jim\u00b4enez-Luna, J.; Grisoni, F.; Schneider,
-        G. Drug discovery with explainable artificial\n\n       intelligence. Nature
-        Machine Intelligence 2020 2:10 2020, 2, 573\u2013584.\n\n\n(102) Fu, T.; Gao,
-        W.; Xiao, C.; Yasonik, J.; Coley, C. W.; Sun, J. Differentiable Scaffold-\n\n      ing
-        Tree for Molecule Optimization. International Conference on Learning Represen-\n\n       tations.
-        2022.\n\n\n(103) Shen, C.; Krenn, M.; Eppel, S.; Aspuru-Guzik, A. Deep molecular
-        dreaming: inverse\n\n     machine learning for de-novo molecular design and
-        interpretability with surjective\n\n      representations. Machine Learning:
-        Science and Technology 2021, 2, 03LT02.\n\n\n(104) Lucic,  A.;   ter  Hoeve,  M.;   Tolomei,  G.;   Rijke,  M.;   Silvestri,  F.  CF-\n\n     GNNExplainer:  Counterfactual
-        Explanations for Graph Neural Networks. arXiv\n\n      pre\n\n----\n\nQuestion:
-        Are counterfactuals actionable? [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20",
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+        leading peer-reviewed journal.\n\n----\n\n315\u20131360.\n\n\n (9) Wellawatte,
+        G. P.; Seshadri, A.; White, A. D. Model agnostic generation of counter-\n\n     factual
+        explanations for molecules. Chemical Science 2022, 13, 3697\u20133705.\n\n\n(10)
+        Gandhi, H. A.; White, A. D. Explaining structure-activity relationships using
+        locally\n\n      faithful surrogate models. chemrxiv 2022,\n\n\n(11) Gormley,
+        A. J.; Webb, M. A. Machine learning in combinatorial polymer chemistry.\n\n     Nature
+        Reviews Materials 2021,\n\n\n(12) Gomes, C. P.; Fink, D.; Dover, R. B. V.; Gregoire,
+        J. M. Computational sustainability\n\n     meets materials science. Nature Reviews
+        Materials 2021,\n\n\n(13) On scientific understanding with artificial intelligence.
+        Nature Reviews Physics 2022\n\n     4:12 2022, 4, 761\u2013769.\n\n\n(14) Arrieta,
+        A. B.; D\u00b4\u0131az-Rodr\u00b4\u0131guez, N.; Ser, J. D.; Bennetot, A.; Tabik,
+        S.; Barbado, A.;\n\n     Garcia, S.; Gil-Lopez, S.; Molina, D.; Benjamins, R.;
+        Chatila, R.; Herrera, F. Explain-\n\n     able Artificial Intelligence (XAI):
+        Concepts, Taxonomies, Opportunities and Chal-\n\n     lenges toward Responsible
+        AI. Information Fusion 2019, 58, 82\u2013115.\n\n\n(15) Murdoch, W. J.; Singh,
+        C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B. Interpretable machine\n\n     learning:
+        definitions, methods, and applications. ArXiv 2019, abs/1901.04592.\n\n\n                                      25(16)
+        Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility
+        better\n\n     than computers? Journal of cheminformatics 2017, 9, 1\u201314.\n\n\n(17)
+        Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance.
+        Human\n\n     Factors 2004, 46, 50\u201380.\n\n\n(18) Bolukbasi, T.; Chang,
+        K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\n\n     puter
+        programmer as woman is to homemaker? debiasing word embeddings. Advances\n\n     in
+        neural information processing systems 2016, 29.\n\n\n(19) Buolamwini, J.; Gebru,
+        T. Gender Shades:  Intersectional Accuracy Disparities in\n\n    Commercial
+        Gender Classification. Proceedings of the 1st Conference on Fairness,\n\n     Accountability
+        and Transparency. 2018; pp 77\u201391.\n\n\n(20) Lapuschkin, S.; W\u00a8aldchen,
+        S.; Binder, A.; Montavon, G.; Samek, W.; M\u00a8uller, K.-R.\n\n    Unmasking
+        Clever Hans predictors and assessing what machines really learn. Nature\n\n     communications
+        2019, 10, 1\u20138.\n\n\n(21) DeGrave, A. J.; Janizek, J. D.; Lee, S.-I. AI
+        for radiographic COVID-19 detection\n\n      selects shortcuts over signal.
+        Nature Machine Intelligence 2021, 3, 610\u2013619.\n\n\n(22) Goodman, B.; Flaxman,
+        S. European Union regulations on algorithmic decision-\n\n    making and a \u201cright
+        to explanation\u201d. AI Magazine 2017, 38, 50\u201357.\n\n\n(23) ACT, A. I.
+        European Commission. On Artificial Intelligence: A European Approach\n\n     to
+        Excellence and Trust. 2021, COM/2021/206.\n\n\n(24) Blueprint for an AI Bill
+        of Rights, The White House. 2022; https://www.whitehouse.\n\n    gov/ostp/ai-bill-of-rights/.\n\n\n(25)
+        Miller, T. Explanation in artificial intelligence: Insights from the social
+        sciences. Ar-\n\n       tificial intelligence 2019, 267, 1\u201338.\n\n\n\n                                      26(26)
+        Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B. Definitions,
+        meth-\n\n     ods, and applications in interpretable machine learning. Proceedings
+        of the National\n\n    Academy of Sciences of the United States of America 2019,
+        116, 22071\u201322080.\n\n\n(27) Gunning, D.; Aha, D. DARPA\u2019s Explainable
+        Artificial Intelligence (XAI) Program.\n\n    AI Magazine 2019, 40, 44\u201358.\n\n\n(28)
+        Biran, O.; Cotton, C. Explanation and justification in machine learning: A survey.\n\n     IJCAI-17
+        workshop on explainable AI (XAI). 2017; pp 8\u201313.\n\n\n(29) Palacio, S.;
+        Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\n\n    Towards
+        a unified framework for explainable ai. Proceedings of the IEEE/CVF Inter-\n\n     national
+        Conference on Computer Vision. 2021; pp 3766\u20133775.\n\n\n(30) Kuhn, D. R.;
+        Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for Ex-\n\n     plainable
+        AI. 2020 IEEE International Conference on Software Testing, Verification\n\n    and
+        Validation Workshops (ICSTW) 2020, 167\u2013170.\n\n\n(31) Seshadri, A.; Gandhi,
+        H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\n\n     smell?
+        ChemRxiv 2022,\n\n\n(32) Das, A.; Rad, P. Opportunities and challenges in explainable
+        artificial intelligence\n\n      (xai): A survey. arXiv preprint arXiv:2006.11371
+        2020,\n\n\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, K. Y.; Belikov,
+        J.; Mannor, S.; Levron, Y.\n\n     Explainable Artificial Intelligence (XAI)
+        techniques for energy and power systems:\n\n     Review, challenges and opportunities.
+        Energy and AI 2022, 9, 100169.\n\n\n(34) Koh, P. W.; Liang, P. Understanding
+        black-box predictions via influence functions.\n\n     International Conference
+        on Machine Learning. 2017; pp 1885\u20131894.\n\n\n(35) Ribeiro, M. T.; Singh,
+        S.; Guestrin, C. \u201d Why should i trust you?\u201d Explaining the\n\n     predictions
+        of any classifier. Proceedings of the 22nd ACM SIGKDD international\n\n\n                                      27     conference
+        on knowledge discovery and data mining. San\n\n----\n\nQuestion: Are counterfactuals
+        actionable? [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20", "n": 1, "temperature":
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         100 words words. `relevance_score` is an integer 1-10 for the relevance of `summary`
         to the question.\n"}, {"role": "user", "content": "Excerpt from wellawatteUnknownyearaperspectiveon
-        pages 5-8: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
+        pages 1-3: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
         White. A perspective on explanations of molecular prediction models. Journal
         of Chemical Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
         doi:10.1021/acs.jctc.2c01235. This article has 23 citations and is from a domain
-        leading peer-reviewed journal.\n\n----\n\n  We present an example evaluation
-        of the SHAP explanation method based on the above\n\nattributes.44 Shapley values
-        were proposed as a local explanation method based on feature\n\nattribution,
-        as they offer a complete explanation - each feature is assigned a fraction of\n\nthe
-        prediction value.44,45 Completeness is a clearly measurable and well-defined
-        metric, but\n\nyields explanations with many components. Yet Shapley values
-        are not actionable nor sparse.\n\nThey are non-sparse as every feature has a
-        non-zero attribution and not-actionable because\n\nthey do not provide a set
-        of features which changes the outcome.46 Ribeiro et al. 35 proposed\n\na surrogate
-        model method that aims to provide sparse/succinct explanations that have high\n\n\n                                       5fidelity
-        to the original model. In Wellawatte et al. 9 we argue that counterfactuals
-        are \u201cbet-\n\nter\u201d explanations because they are actionable and sparse.
-        We highlight that, evaluation of\n\nexplanations is a difficult task because
-        explanations are fundamentally for and by humans.\n\nTherefore, these evaluations
-        are subjective, as they depend on \u201ccomplex human factors and\n\napplication
-        scenarios.\u201d37\n\n\nSelf-explaining models\n\nA self-explanatory model is
-        one that is intrinsically interpretable to an expert.47 Two com-\n\nmon examples
-        found in the literature are linear regression models and decision trees (DT).\n\nIntrinsic
-        models can be found in other XAI applications acting as surrogate models (proxy\n\nmodels)
-        due to their transparent nature.48,49 A linear model is described by the equation\n\n1
-        where, W\u2019s are the weight parameters and x\u2019s are the input features
-        associated with the\n\nprediction \u02c6y. Therefore, we observe that the weights
-        can be used to derive a complete expla-\n\nnation of the model - trained weights
-        quantify the importance of each feature.47 DT models\n\nare another type of
-        self-explaining models which have been used in classification and high-\n\nthroughput
-        screening tasks.  Gajewicz et al. 50 used DT models to classify nanomaterials\n\nthat
-        identify structural features responsible for surface activity. In another study
-        by Han\n\net al. 51, a DT model was developed to filter compounds by their bioactivity
-        based on the\n\nchemical fingerprints.\n\n\n\n                                                              \u02c6y
-        = \u03a3iWixi                                      (1)\n\n\n   Regularization
-        techniques such as EXPO52 and RRR53 are designed to enhance the black-\n\nbox
-        model interpretability.54 Although one can argue that \u201csimplicity\u201d
-        of models are posi-\n\ntively correlated with interpretability, this is based
-        on how the interpretability is evaluated.\n\nFor example, Lipton 55 argue that,
-        from the notion of \u201csimulatability\u201d (the degree to which a\n\nhuman
-        can predict the outcome based on inputs), self-explanatory linear models, rule-based\n\n\n\n                                       6systems,
-        and DT\u2019s can be claimed uninterpretable. A human can predict the outcome
-        given\n\nthe inputs only if the input features are interpretable. Therefore,
-        a linear model which takes\n\nin non-descriptive inputs may not be as transparent.
-        On the other hand, a linear model\n\nis not innately accurate as they fail to
-        capture non-linear relationships in data, limiting is\n\napplicability. Similarly,
-        a DT is a rule-based model and lacks physics informed knowledge.\n\nTherefore,
-        an existing drawback is the trade-offbetween the degree of understandability
-        and\n\nthe accuracy of a model. For example, an intrinsic model (linear regression
-        or decision trees)\n\ncan be described through the trainable parameters, but
-        it may fail to \u201ccorrectly\u201d capture\n\nnon-linear relations in the
-        data.\n\n\nAttribution methods\n\n\nFeature attribution methods explain black
-        box predictions by assigning each input feature\n\na numerical value, which
-        indicates its importance or contribution to the prediction. Feature\n\nattributions
-        provide local explanations, but can be averaged or combined to explain multi-\n\nple
-        instances. Atom-based numerical assignments are commonly referred to as heatmaps.56\n\nSheridan
-        57 describes an atom-wise attribution method for interpreting QSAR models. Re-\n\ncently,
-        Rasmussen et al. 58 showed that Crippen logP models serve as a benchmark for\n\nheatmap
-        approaches. Other most widely used feature attribution approaches in the litera-\n\nture
-        are gradient based methods,59,60 Shapley Additive exPlanations (SHAP),44 and
-        layer-\n\nwise relevance prorogation.61\n\n   Gradient based approaches are
-        based on the hypothesis that gradients for neural net-\n\nworks are analogous
-        to coefficients for regression models.62 Class activation maps (CAM),63\n\ngradCAM,64
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-        idea behind feature attributions with gradients can be represented with equation  2.\n\n                                          \u2206\u02c6f(\u20d7x)
-        \u2248\u2202\u02c6f(\u20d7x)                                    (2)\n                               \u2206xi      \u2202xi\n\n\n\n                                       7                                         \n\n----\n\nQuestion:
-        Are counterfactuals actionable? [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20",
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+        leading peer-reviewed journal.\n\n----\n\n A Perspective on Explanations of
+        Molecular\n\n              Prediction Models\n\n\nGeemi P. Wellawatte,\u2020   Heta
+        A. Gandhi,\u2021   Aditi Seshadri,\u2021 and  Andrew\n\n                           D.
+        White\u2217,\u2021\n\n\n     \u2020Department of Chemistry, University of Rochester,
+        Rochester, NY, 14627\n\n\u2021Department of Chemical Engineering, University
+        of Rochester, Rochester, NY, 14627\n\n             \u00b6Vial Health Technology,
+        Inc., San Francisco, CA 94111\n\n\n                           E-mail: andrew.white@rochester.edu\n\n\n\n                                 Abstract\n\n\n      Chemists
+        can be skeptical in using deep learning (DL) in decision making, due to\n\n   the
+        lack of interpretability in \u201cblack-box\u201d models.  Explainable artificial
+        intelligence\n\n   (XAI) is a branch of AI which addresses this drawback by
+        providing tools to interpret\n\n  DL models and their predictions. We review
+        the principles of XAI in the domain of\n\n   chemistry and emerging methods
+        for creating and evaluating explanations. Then we\n\n   focus on methods developed
+        by our group and their applications in predicting solubil-\n\n    ity, blood-brain
+        barrier permeability, and the scent of molecules. We show that XAI\n\n   methods
+        like chemical counterfactuals and descriptor explanations can explain DL pre-\n\n   dictions
+        while giving insight into structure-property relationships. Finally, we discuss\n\n   how
+        a two-step process of developing a black-box model and explaining predictions
+        can\n\n   uncover structure-property relationships.\n\n\n\n\n\n                                     1Introduction\n\n\nDeep
+        learning (DL) is advancing the boundaries of computational chemistry because
+        it can\n\naccurately model non-linear structure-function relationships.1\u20133
+        Applications of DL can be\n\nfound in a broad spectrum spanning from quantum
+        computing4,5 to drug discovery6\u201310 to\n\nmaterials design.11,12 According
+        to Kre 13, DL models can contribute to scientific discovery\n\nin three \u201cdimensions\u201d
+        - 1) as a \u2018computational microscope\u2019 to gain insight which are not\n\nattainable
+        through experiments 2) as a \u2018resource of inspiration\u2019 to motivate
+        scientific thinking\n\n3) as an \u2018agent of understanding\u2019 to uncover
+        new observations. However, the rationale of\n\na DL prediction is not always
+        apparent due to the model architecture consisting a large\n\nparameter count.14,15
+        DL models are thus often termed\u201cblack box\u201d models. We can only\n\nreason
+        about the input and output of an DL model, not the underlying cause that leads
+        to\n\na specific prediction.\n\n    It is routine in chemistry now for DL to
+        exceed human level performance \u2014 humans are\n\nnot good at predicting solubility
+        from structure for example161 \u2014 and so understanding how\n\na model makes
+        predictions can guide hypotheses. This is in contrast to a topic like finding\n\na
+        stop sign in an image, where there is little new to be learned about visual
+        perception\n\nby explaining a DL model. However, the black box nature of DL
+        has its own limitations.\n\nUsers are more likely to trust and use predictions
+        from a model if they can understand why\n\nthe prediction was made.17 Explaining
+        predictions can help developers of DL models ensure\n\nthe model is not learning
+        spurious correlations.18,19 Two infamous examples are, 1)neural\n\nnetworks
+        that learned to recognize horses by looking for a photographer\u2019s watermark20
+        and,\n\n2) neural networks that predicted a COVID-19 diagnoses by looking at
+        the font choice\n\non medical images.21 As a result, there is an emerging regulatory
+        framework for when any\n\ncomputer algorithms impact humans.22\u201324 Although
+        we know of no examples yet in chemistry,\n\none can assume the use of AI in
+        predicting toxicity, carcinogenicity, and environmental\n\npersistence will
+        require rationale for the predictions due to regulatory consequences.\n\n   1there
+        does happen to be one human solubility savant, participant 11, who matched machine
+        performance\n\n\n                                       2   EXplainable Artificial
+        Intelligence (XAI) is a field of growing importance that aims to\n\nprovide
+        model interpretations of DL predictions Three terms highly associated with XAI
+        are,\n\ninterpretability, justifications and explainability. Miller 25 defines
+        that interpretability of a\n\nmodel refers to the degree of human understandability
+        intrinsic within the model. Murdoch\n\net al. 26 clarify that interpretability
+        can be perceived as \u201cknowledge\u201d which provide insight\n\nto a particular
+        problem.  Justifications are quantitative metrics tell the users \u201cwhy the\n\nmodel
+        should be trusted,\u201d like test error.27 Justifications are evidence which
+        defend why a\n\nprediction is trustworthy.25 An \u201cexplanation\u201d is a
+        description on why a certain prediction was\n\nmade.9,28 Interpretability and
+        explanation are often used interchangeably. Arrieta et al. 14\n\ndistinguish
+        that interpretability is a passive characteristic of a model, whereas explainability\n\nis
+        an active characteristic which is used to clarify the internal decision-making
+        process.\n\nNamely, an explanation is extra information that gives the context
+        and a cause for one or\n\nmore \n\n----\n\nQuestion: Are counterfactuals actionable?
+        [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20", "n": 1, "temperature": 0.0}'
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-        locally\n\n      faithful surrogate models. chemrxiv 2022,\n\n\n(11) Gormley,
-        A. J.; Webb, M. A. Machine learning in combinatorial polymer chemistry.\n\n     Nature
-        Reviews Materials 2021,\n\n\n(12) Gomes, C. P.; Fink, D.; Dover, R. B. V.; Gregoire,
-        J. M. Computational sustainability\n\n     meets materials science. Nature Reviews
-        Materials 2021,\n\n\n(13) On scientific understanding with artificial intelligence.
-        Nature Reviews Physics 2022\n\n     4:12 2022, 4, 761\u2013769.\n\n\n(14) Arrieta,
-        A. B.; D\u00b4\u0131az-Rodr\u00b4\u0131guez, N.; Ser, J. D.; Bennetot, A.; Tabik,
-        S.; Barbado, A.;\n\n     Garcia, S.; Gil-Lopez, S.; Molina, D.; Benjamins, R.;
-        Chatila, R.; Herrera, F. Explain-\n\n     able Artificial Intelligence (XAI):
-        Concepts, Taxonomies, Opportunities and Chal-\n\n     lenges toward Responsible
-        AI. Information Fusion 2019, 58, 82\u2013115.\n\n\n(15) Murdoch, W. J.; Singh,
-        C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B. Interpretable machine\n\n     learning:
-        definitions, methods, and applications. ArXiv 2019, abs/1901.04592.\n\n\n                                      25(16)
-        Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility
-        better\n\n     than computers? Journal of cheminformatics 2017, 9, 1\u201314.\n\n\n(17)
-        Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance.
-        Human\n\n     Factors 2004, 46, 50\u201380.\n\n\n(18) Bolukbasi, T.; Chang,
-        K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\n\n     puter
-        programmer as woman is to homemaker? debiasing word embeddings. Advances\n\n     in
-        neural information processing systems 2016, 29.\n\n\n(19) Buolamwini, J.; Gebru,
-        T. Gender Shades:  Intersectional Accuracy Disparities in\n\n    Commercial
-        Gender Classification. Proceedings of the 1st Conference on Fairness,\n\n     Accountability
-        and Transparency. 2018; pp 77\u201391.\n\n\n(20) Lapuschkin, S.; W\u00a8aldchen,
-        S.; Binder, A.; Montavon, G.; Samek, W.; M\u00a8uller, K.-R.\n\n    Unmasking
-        Clever Hans predictors and assessing what machines really learn. Nature\n\n     communications
-        2019, 10, 1\u20138.\n\n\n(21) DeGrave, A. J.; Janizek, J. D.; Lee, S.-I. AI
-        for radiographic COVID-19 detection\n\n      selects shortcuts over signal.
-        Nature Machine Intelligence 2021, 3, 610\u2013619.\n\n\n(22) Goodman, B.; Flaxman,
-        S. European Union regulations on algorithmic decision-\n\n    making and a \u201cright
-        to explanation\u201d. AI Magazine 2017, 38, 50\u201357.\n\n\n(23) ACT, A. I.
-        European Commission. On Artificial Intelligence: A European Approach\n\n     to
-        Excellence and Trust. 2021, COM/2021/206.\n\n\n(24) Blueprint for an AI Bill
-        of Rights, The White House. 2022; https://www.whitehouse.\n\n    gov/ostp/ai-bill-of-rights/.\n\n\n(25)
-        Miller, T. Explanation in artificial intelligence: Insights from the social
-        sciences. Ar-\n\n       tificial intelligence 2019, 267, 1\u201338.\n\n\n\n                                      26(26)
-        Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B. Definitions,
-        meth-\n\n     ods, and applications in interpretable machine learning. Proceedings
-        of the National\n\n    Academy of Sciences of the United States of America 2019,
-        116, 22071\u201322080.\n\n\n(27) Gunning, D.; Aha, D. DARPA\u2019s Explainable
-        Artificial Intelligence (XAI) Program.\n\n    AI Magazine 2019, 40, 44\u201358.\n\n\n(28)
-        Biran, O.; Cotton, C. Explanation and justification in machine learning: A survey.\n\n     IJCAI-17
-        workshop on explainable AI (XAI). 2017; pp 8\u201313.\n\n\n(29) Palacio, S.;
-        Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\n\n    Towards
-        a unified framework for explainable ai. Proceedings of the IEEE/CVF Inter-\n\n     national
-        Conference on Computer Vision. 2021; pp 3766\u20133775.\n\n\n(30) Kuhn, D. R.;
-        Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for Ex-\n\n     plainable
-        AI. 2020 IEEE International Conference on Software Testing, Verification\n\n    and
-        Validation Workshops (ICSTW) 2020, 167\u2013170.\n\n\n(31) Seshadri, A.; Gandhi,
-        H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\n\n     smell?
-        ChemRxiv 2022,\n\n\n(32) Das, A.; Rad, P. Opportunities and challenges in explainable
-        artificial intelligence\n\n      (xai): A survey. arXiv preprint arXiv:2006.11371
-        2020,\n\n\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, K. Y.; Belikov,
-        J.; Mannor, S.; Levron, Y.\n\n     Explainable Artificial Intelligence (XAI)
-        techniques for energy and power systems:\n\n     Review, challenges and opportunities.
-        Energy and AI 2022, 9, 100169.\n\n\n(34) Koh, P. W.; Liang, P. Understanding
-        black-box predictions via influence functions.\n\n     International Conference
-        on Machine Learning. 2017; pp 1885\u20131894.\n\n\n(35) Ribeiro, M. T.; Singh,
-        S.; Guestrin, C. \u201d Why should i trust you?\u201d Explaining the\n\n     predictions
-        of any classifier. Proceedings of the 22nd ACM SIGKDD international\n\n\n                                      27     conference
-        on knowledge discovery and data mining. San\n\n----\n\nQuestion: Are counterfactuals
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+        A.; Pasculli, G.; Feldmann, C.; Rodr\u00b4\u0131guez-P\u00b4erez, R.; Bajorath,
+        J. Edge-\n\n    SHAPer: Bond-Centric Shapley Value-Based Explanation Method
+        for Graph Neural\n\n     Networks. iScience 2022, 25, 105043.\n\n\n(79) White,
+        A. D. Deep learning for molecules and materials. Living Journal of Computa-\n\n      tional
+        Molecular Science 2022, 3.\n\n(80) \u02d8Strumbelj, E.; Kononenko, I. Explaining
+        prediction models and individual predictions\n\n     with feature contributions.
+        Knowledge and Information Systems 2014, 41, 647\u2013665.\n\n\n(81) Erhan, D.;
+        Bengio, Y.; Courville, A.; Vincent, P. Visualizing Higher-Layer Features of\n\n     a
+        Deep Network. Technical Report, Univerist\u00b4e de Montr\u00b4eal 2009,\n\n\n(82)
+        Weber, J. K.; Morrone, J. A.; Bagchi, S.; Pabon, J. D.; gu Kang, S.; Zhang,
+        L.;\n\n     Cornell, W. D. Simplified, interpretable graph convolutional neural
+        networks for small\n\n     molecule activity prediction. Journal of Computer-Aided
+        Molecular Design 2022, 36,\n\n     391\u2013404.\n\n\n(83) Riniker, S.; Landrum,
+        G. A. Similarity maps - A visualization strategy for molecular\n\n     fingerprints
+        and machine-learning methods. Journal of Cheminformatics 2013, 5, 1\u20137.\n\n\n(84)
+        Humer, C.; Heberle, H.; Montanari, F.; Wolf, T.; Huber, F.; Henderson, R.; Hein-\n\n      rich,
+        J.; Streit, M. ChemInformatics Model Explorer (CIME): exploratory analysis of\n\n     chemical
+        model explanations. Journal of Cheminformatics 2022, 14, 1\u201314.\n\n\n(85)
+        McGrath, T.; Kapishnikov, A.; Toma\u02c7sev, N.; Pearce, A.; Wattenberg, M.;
+        Hass-\n\n      abis, D.; Kim, B.; Paquet, U.; Kramnik, V. Acquisition of chess
+        knowledge in Al-\n\n     phaZero. Proceedings of the National Academy of Sciences
+        2022, 119, e2206625119.\n\n\n\n\n                                      33(86)
+        Bajusz, D.; R\u00b4acz, A.; H\u00b4eberger, K. Why is Tanimoto index an appropriate
+        choice for\n\n     fingerprint-based similarity calculations? Journal of Cheminformatics
+        2015, 7, 1\u201313.\n\n\n(87) Huang, Q.; Yamada, M.; Tian, Y.; Singh, D.; Yin,
+        D.; Chang, Y. GraphLIME:\n\n     Local Interpretable Model Explanations for
+        Graph Neural Networks. CoRR 2020,\n\n     abs/2001.06216.\n\n\n(88) Sokol, K.;
+        Flach, P. A. LIMEtree: Interactively Customisable Explanations Based on\n\n     Local
+        Surrogate Multi-output Regression Trees. CoRR 2020, abs/2005.01427.\n\n\n(89)
+        Whitmore, L. S.; George, A.; Hudson, C. M. Mapping chemical performance on molec-\n\n     ular
+        structures using locally interpretable explanations. 2016; https://arxiv.org/\n\n    abs/1611.07443.\n\n\n(90)
+        Mehdi, S.; Tiwary, P. Thermodynamics of Interpretation. 2022,\n\n\n(91) H\u00a8ofler,
+        M. Causal inference based on counterfactuals. BMC Medical Research Method-\n\n     ology
+        2005, 5, 1\u201312.\n\n\n(92) Woodward, J.; Hitchcock, C. Explanatory Generalizations,
+        Part I: A Counterfactual\n\n     Account. No\u02c6us 2003, 37, 1\u201324.\n\n\n(93)
+        Frisch, M. F. Theories, models, and explanation; University of California, Berkeley,\n\n     1998.\n\n\n(94)
+        Reutlinger, A. Is There A Monist Theory of Causal and Non-Causal Explanations?\n\n    The
+        Counterfactual Theory of Scientific Explanation. Philosophy of Science 2016,
+        83,\n\n     733\u2013745.\n\n\n(95) Lewis, D. Causation. The journal of philosophy
+        1974, 70, 556\u2013567.\n\n\n(96) Tanimoto, T. T. Elementary mathematical theory
+        of classification and prediction.\n\n     Internal IBM Technical Report 1958,\n\n\n                                      34
+        (97) Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. Journal of Chemical
+        In-\n\n      formation and Modeling 2010, 50, 742\u2013754, PMID: 20426451.\n\n\n
+        (98) Mohapatra, S.; An, J.; G\u00b4omez-Bombarelli, R. Chemistry-informed macromolecule\n\n      graph
+        representation for similarity computation, unsupervised and supervised learn-\n\n       ing.
+        Machine Learning: Science and Technology 2022, 3, 015028.\n\n\n (99) Doshi-Velez,
+        F.; Kortz, M.; Budish, R.; Bavitz, C.; Gershman, S.; O\u2019Brien, D.;\n\n       Scott,
+        K.; Schieber, S.; Waldo, J.; Weinberger, D.; Weller, A.; Wood, A. Account-\n\n       ability
+        of AI Under the Law: The Role of Explanation. SSRN Electronic Journal\n\n     2017,\n\n\n(100)
+        Wachter, S.; Mittelstadt, B.; Russell, C. Counterfactual explanations without
+        opening\n\n      the black box: Automated decisions and the GDPR. Harv. JL &
+        Tech. 2017, 31, 841.\n\n\n(101) Jim\u00b4enez-Luna, J.; Grisoni, F.; Schneider,
+        G. Drug discovery with explainable artificial\n\n       intelligence. Nature
+        Machine Intelligence 2020 2:10 2020, 2, 573\u2013584.\n\n\n(102) Fu, T.; Gao,
+        W.; Xiao, C.; Yasonik, J.; Coley, C. W.; Sun, J. Differentiable Scaffold-\n\n      ing
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+        2022.\n\n\n(103) Shen, C.; Krenn, M.; Eppel, S.; Aspuru-Guzik, A. Deep molecular
+        dreaming: inverse\n\n     machine learning for de-novo molecular design and
+        interpretability with surjective\n\n      representations. Machine Learning:
+        Science and Technology 2021, 2, 03LT02.\n\n\n(104) Lucic,  A.;   ter  Hoeve,  M.;   Tolomei,  G.;   Rijke,  M.;   Silvestri,  F.  CF-\n\n     GNNExplainer:  Counterfactual
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-        for creating and evaluating explanations. Then we\n\n   focus on methods developed
-        by our group and their applications in predicting solubil-\n\n    ity, blood-brain
-        barrier permeability, and the scent of molecules. We show that XAI\n\n   methods
-        like chemical counterfactuals and descriptor explanations can explain DL pre-\n\n   dictions
-        while giving insight into structure-property relationships. Finally, we discuss\n\n   how
-        a two-step process of developing a black-box model and explaining predictions
-        can\n\n   uncover structure-property relationships.\n\n\n\n\n\n                                     1Introduction\n\n\nDeep
-        learning (DL) is advancing the boundaries of computational chemistry because
-        it can\n\naccurately model non-linear structure-function relationships.1\u20133
-        Applications of DL can be\n\nfound in a broad spectrum spanning from quantum
-        computing4,5 to drug discovery6\u201310 to\n\nmaterials design.11,12 According
-        to Kre 13, DL models can contribute to scientific discovery\n\nin three \u201cdimensions\u201d
-        - 1) as a \u2018computational microscope\u2019 to gain insight which are not\n\nattainable
-        through experiments 2) as a \u2018resource of inspiration\u2019 to motivate
-        scientific thinking\n\n3) as an \u2018agent of understanding\u2019 to uncover
-        new observations. However, the rationale of\n\na DL prediction is not always
-        apparent due to the model architecture consisting a large\n\nparameter count.14,15
-        DL models are thus often termed\u201cblack box\u201d models. We can only\n\nreason
-        about the input and output of an DL model, not the underlying cause that leads
-        to\n\na specific prediction.\n\n    It is routine in chemistry now for DL to
-        exceed human level performance \u2014 humans are\n\nnot good at predicting solubility
-        from structure for example161 \u2014 and so understanding how\n\na model makes
-        predictions can guide hypotheses. This is in contrast to a topic like finding\n\na
-        stop sign in an image, where there is little new to be learned about visual
-        perception\n\nby explaining a DL model. However, the black box nature of DL
-        has its own limitations.\n\nUsers are more likely to trust and use predictions
-        from a model if they can understand why\n\nthe prediction was made.17 Explaining
-        predictions can help developers of DL models ensure\n\nthe model is not learning
-        spurious correlations.18,19 Two infamous examples are, 1)neural\n\nnetworks
-        that learned to recognize horses by looking for a photographer\u2019s watermark20
-        and,\n\n2) neural networks that predicted a COVID-19 diagnoses by looking at
-        the font choice\n\non medical images.21 As a result, there is an emerging regulatory
-        framework for when any\n\ncomputer algorithms impact humans.22\u201324 Although
-        we know of no examples yet in chemistry,\n\none can assume the use of AI in
-        predicting toxicity, carcinogenicity, and environmental\n\npersistence will
-        require rationale for the predictions due to regulatory consequences.\n\n   1there
-        does happen to be one human solubility savant, participant 11, who matched machine
-        performance\n\n\n                                       2   EXplainable Artificial
-        Intelligence (XAI) is a field of growing importance that aims to\n\nprovide
-        model interpretations of DL predictions Three terms highly associated with XAI
-        are,\n\ninterpretability, justifications and explainability. Miller 25 defines
-        that interpretability of a\n\nmodel refers to the degree of human understandability
-        intrinsic within the model. Murdoch\n\net al. 26 clarify that interpretability
-        can be perceived as \u201cknowledge\u201d which provide insight\n\nto a particular
-        problem.  Justifications are quantitative metrics tell the users \u201cwhy the\n\nmodel
-        should be trusted,\u201d like test error.27 Justifications are evidence which
-        defend why a\n\nprediction is trustworthy.25 An \u201cexplanation\u201d is a
-        description on why a certain prediction was\n\nmade.9,28 Interpretability and
-        explanation are often used interchangeably. Arrieta et al. 14\n\ndistinguish
-        that interpretability is a passive characteristic of a model, whereas explainability\n\nis
-        an active characteristic which is used to clarify the internal decision-making
-        process.\n\nNamely, an explanation is extra information that gives the context
-        and a cause for one or\n\nmore \n\n----\n\nQuestion: Are counterfactuals actionable?
-        [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20", "temperature": 0.0}'
+        leading peer-reviewed journal.\n\n----\n\n  We present an example evaluation
+        of the SHAP explanation method based on the above\n\nattributes.44 Shapley values
+        were proposed as a local explanation method based on feature\n\nattribution,
+        as they offer a complete explanation - each feature is assigned a fraction of\n\nthe
+        prediction value.44,45 Completeness is a clearly measurable and well-defined
+        metric, but\n\nyields explanations with many components. Yet Shapley values
+        are not actionable nor sparse.\n\nThey are non-sparse as every feature has a
+        non-zero attribution and not-actionable because\n\nthey do not provide a set
+        of features which changes the outcome.46 Ribeiro et al. 35 proposed\n\na surrogate
+        model method that aims to provide sparse/succinct explanations that have high\n\n\n                                       5fidelity
+        to the original model. In Wellawatte et al. 9 we argue that counterfactuals
+        are \u201cbet-\n\nter\u201d explanations because they are actionable and sparse.
+        We highlight that, evaluation of\n\nexplanations is a difficult task because
+        explanations are fundamentally for and by humans.\n\nTherefore, these evaluations
+        are subjective, as they depend on \u201ccomplex human factors and\n\napplication
+        scenarios.\u201d37\n\n\nSelf-explaining models\n\nA self-explanatory model is
+        one that is intrinsically interpretable to an expert.47 Two com-\n\nmon examples
+        found in the literature are linear regression models and decision trees (DT).\n\nIntrinsic
+        models can be found in other XAI applications acting as surrogate models (proxy\n\nmodels)
+        due to their transparent nature.48,49 A linear model is described by the equation\n\n1
+        where, W\u2019s are the weight parameters and x\u2019s are the input features
+        associated with the\n\nprediction \u02c6y. Therefore, we observe that the weights
+        can be used to derive a complete expla-\n\nnation of the model - trained weights
+        quantify the importance of each feature.47 DT models\n\nare another type of
+        self-explaining models which have been used in classification and high-\n\nthroughput
+        screening tasks.  Gajewicz et al. 50 used DT models to classify nanomaterials\n\nthat
+        identify structural features responsible for surface activity. In another study
+        by Han\n\net al. 51, a DT model was developed to filter compounds by their bioactivity
+        based on the\n\nchemical fingerprints.\n\n\n\n                                                              \u02c6y
+        = \u03a3iWixi                                      (1)\n\n\n   Regularization
+        techniques such as EXPO52 and RRR53 are designed to enhance the black-\n\nbox
+        model interpretability.54 Although one can argue that \u201csimplicity\u201d
+        of models are posi-\n\ntively correlated with interpretability, this is based
+        on how the interpretability is evaluated.\n\nFor example, Lipton 55 argue that,
+        from the notion of \u201csimulatability\u201d (the degree to which a\n\nhuman
+        can predict the outcome based on inputs), self-explanatory linear models, rule-based\n\n\n\n                                       6systems,
+        and DT\u2019s can be claimed uninterpretable. A human can predict the outcome
+        given\n\nthe inputs only if the input features are interpretable. Therefore,
+        a linear model which takes\n\nin non-descriptive inputs may not be as transparent.
+        On the other hand, a linear model\n\nis not innately accurate as they fail to
+        capture non-linear relationships in data, limiting is\n\napplicability. Similarly,
+        a DT is a rule-based model and lacks physics informed knowledge.\n\nTherefore,
+        an existing drawback is the trade-offbetween the degree of understandability
+        and\n\nthe accuracy of a model. For example, an intrinsic model (linear regression
+        or decision trees)\n\ncan be described through the trainable parameters, but
+        it may fail to \u201ccorrectly\u201d capture\n\nnon-linear relations in the
+        data.\n\n\nAttribution methods\n\n\nFeature attribution methods explain black
+        box predictions by assigning each input feature\n\na numerical value, which
+        indicates its importance or contribution to the prediction. Feature\n\nattributions
+        provide local explanations, but can be averaged or combined to explain multi-\n\nple
+        instances. Atom-based numerical assignments are commonly referred to as heatmaps.56\n\nSheridan
+        57 describes an atom-wise attribution method for interpreting QSAR models. Re-\n\ncently,
+        Rasmussen et al. 58 showed that Crippen logP models serve as a benchmark for\n\nheatmap
+        approaches. Other most widely used feature attribution approaches in the litera-\n\nture
+        are gradient based methods,59,60 Shapley Additive exPlanations (SHAP),44 and
+        layer-\n\nwise relevance prorogation.61\n\n   Gradient based approaches are
+        based on the hypothesis that gradients for neural net-\n\nworks are analogous
+        to coefficients for regression models.62 Class activation maps (CAM),63\n\ngradCAM,64
+        smoothGrad,,65 and integrated gradients62 are examples of this method. The\n\nmain
+        idea behind feature attributions with gradients can be represented with equation  2.\n\n                                          \u2206\u02c6f(\u20d7x)
+        \u2248\u2202\u02c6f(\u20d7x)                                    (2)\n                               \u2206xi      \u2202xi\n\n\n\n                                       7                                         \n\n----\n\nQuestion:
+        Are counterfactuals actionable? [yes/no]\n\n"}], "model": "gpt-4o-2024-11-20",
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         instance-level explanations that suggest which features can be altered to change
         the outcome. For example, in chemistry, changing a hydrophobic functional group
         in a molecule to a hydrophilic group can increase solubility. This actionability
-        allows users to understand how specific changes in features can lead to different
-        predictions, making counterfactuals a useful tool in explainable AI (XAI).\nFrom
-        Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A
-        perspective on explanations of molecular prediction models. Journal of Chemical
-        Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
+        makes counterfactuals a useful tool for explainable AI (XAI) in chemistry, as
+        they offer intuitive understanding and uncover spurious relationships in training
+        data.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        D. White. A perspective on explanations of molecular prediction models. Journal
+        of Chemical Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
         doi:10.1021/acs.jctc.2c01235. This article has 23 citations and is from a domain
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         14-16: Counterfactuals are actionable as they suggest modifications to molecules
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         the carboxylic acid group of a molecule could enable it to permeate the BBB.
         This aligns with experimental findings that hydrophobic interactions and surface
         area govern BBB permeation. Counterfactuals provide actionable insights by proposing
-        structural changes to improve molecular properties, making them valuable in
-        drug discovery and other applications.\nFrom Geemi P. Wellawatte, Heta A. Gandhi,
+        structural changes to molecules, making them interpretable and useful for chemists
+        in drug development and discovery.\nFrom Geemi P. Wellawatte, Heta A. Gandhi,
         Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular
         prediction models. Journal of Chemical Theory and Computation, Unknown year.
         URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
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         sparse, and provide clear guidance on how to modify molecular structures to
         achieve desired properties. For example, counterfactuals indicate structural
         changes to molecules that would alter their predicted properties, such as scent.
-        This actionable nature makes them useful for tasks like classification and regression
-        in molecular prediction models.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi
+        This actionable nature makes them useful for tasks like model interpretation
+        and guiding chemical design.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi
         Seshadri, and Andrew D. White. A perspective on explanations of molecular prediction
         models. Journal of Chemical Theory and Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235,
         doi:10.1021/acs.jctc.2c01235. This article has 23 citations and is from a domain
         leading peer-reviewed journal.\n\nwellawatteUnknownyearaperspectiveon pages
-        3-5: Counterfactuals are categorized as local interpretations in Explainable
-        AI (XAI) because they explain specific instances. They are considered actionable
-        as they provide insights into how input features can be changed to modify the
-        output. This aligns with the attribute of ''actionable'' explanations, which
-        clarify how input features can be adjusted to influence predictions.\nFrom Geemi
-        P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective
-        on explanations of molecular prediction models. Journal of Chemical Theory and
-        Computation, Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
+        5-8: The excerpt states that counterfactuals are considered ''better'' explanations
+        because they are actionable and sparse. Unlike Shapley values, which are non-sparse
+        and not actionable, counterfactuals provide a set of features that can change
+        the outcome, making them more practical for decision-making. The authors emphasize
+        that counterfactuals are designed to offer actionable insights, which is a key
+        advantage over other explanation methods.\nFrom Geemi P. Wellawatte, Heta A.
+        Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of
+        molecular prediction models. Journal of Chemical Theory and Computation, Unknown
+        year. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
         This article has 23 citations and is from a domain leading peer-reviewed journal.\n\nwellawatteUnknownyearaperspectiveon
-        pages 5-8: The excerpt states that counterfactuals are considered ''better''
-        explanations because they are actionable and sparse. Actionable explanations
-        provide a set of features that can change the outcome, making them useful for
-        decision-making. This contrasts with Shapley values, which are not actionable
-        as they do not provide such a set of features.\nFrom Geemi P. Wellawatte, Heta
-        A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
+        pages 16-20: The excerpt discusses the use of counterfactuals in molecular prediction
+        models, particularly for solubility and scent prediction. Counterfactuals are
+        generated to explore local chemical space and identify structural modifications
+        that influence properties like solubility and scent. For solubility, changes
+        to ester groups, heteroatoms, and substructures like acidic/basic groups are
+        highlighted as impactful. For scent prediction, counterfactuals are used to
+        identify structural changes that alter scent classifications. These findings
+        align with known chemical principles, suggesting that counterfactuals provide
+        actionable insights into modifying molecular properties.\nFrom Geemi P. Wellawatte,
+        Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
         of molecular prediction models. Journal of Chemical Theory and Computation,
         Unknown year. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
         This article has 23 citations and is from a domain leading peer-reviewed journal.\n\nValid
         Keys: wellawatteUnknownyearaperspectiveon pages 12-14, wellawatteUnknownyearaperspectiveon
         pages 14-16, wellawatteUnknownyearaperspectiveon pages 20-22, wellawatteUnknownyearaperspectiveon
-        pages 3-5, wellawatteUnknownyearaperspectiveon pages 5-8\n\n----\n\nQuestion:
+        pages 5-8, wellawatteUnknownyearaperspectiveon pages 16-20\n\n----\n\nQuestion:
         Are counterfactuals actionable? [yes/no]\n\nWrite an answer based on the context.
         If the context provides insufficient information reply \"I cannot answer.\"
         For each part of your answer, indicate which sources most support it via citation
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diff --git a/tests/test_agents.py b/tests/test_agents.py
index 50b0a923..af94cf02 100644
--- a/tests/test_agents.py
+++ b/tests/test_agents.py
@@ -351,12 +351,12 @@ async def on_agent_action(  # noqa: RUF029
     async def llm_model_call(*args, **kwargs):
         # NOTE: "required" will not lead to thoughts being emitted, it has to be "auto"
         # https://docs.anthropic.com/en/docs/build-with-claude/tool-use#chain-of-thought
-        kwargs.pop("tool_choice", LiteLLMModel.TOOL_CHOICE_REQUIRED)
+        # kwargs.pop("tool_choice", LiteLLMModel.TOOL_CHOICE_REQUIRED)
+        # tool_choice is now a arg, not a kwarg
+        args.pop()  # removing it from args # ASK: I accept ideas on how to handle this better
         return await orig_llm_model_call(*args, tool_choice="auto", **kwargs)  # type: ignore[misc]
 
-    with patch.object(
-        LiteLLMModel, "call", side_effect=llm_model_call, autospec=True
-    ):
+    with patch.object(LiteLLMModel, "call", side_effect=llm_model_call, autospec=True):
         response = await agent_query(
             query,
             agent_test_settings,
diff --git a/tests/test_paperqa.py b/tests/test_paperqa.py
index 7f762883..b09b2331 100644
--- a/tests/test_paperqa.py
+++ b/tests/test_paperqa.py
@@ -14,6 +14,7 @@
 import httpx
 import numpy as np
 import pytest
+from aviary.core import Message
 from llmclient import (
     CommonLLMNames,
     Embeddable,
@@ -426,17 +427,23 @@ def test_llm_parse_json_newlines() -> None:
 
 @pytest.mark.asyncio
 async def test_chain_completion() -> None:
-    s = Settings(llm="babbage-002", temperature=0.2)
+    s = Settings(
+        llm="babbage-002", temperature=0.2
+    )
     outputs = []
 
     def accum(x) -> None:
         outputs.append(x)
 
     llm = s.get_llm()
-    completion = await llm.run_prompt(
-        prompt="The {animal} says",
-        data={"animal": "duck"},
-        system_prompt=None,
+
+    prompt = "The {animal} says"
+    data = {"animal": "duck"}
+    messages = [
+        Message(role="user", content=prompt.format(**data)),
+    ]
+    completion = await llm.call_single(  # run_prompt is deprecated
+        messages=messages,
         callbacks=[accum],
     )
     assert completion.seconds_to_first_token > 0
@@ -444,8 +451,8 @@ def accum(x) -> None:
     assert completion.completion_count > 0
     assert str(completion) == "".join(outputs)
 
-    completion = await llm.run_prompt(
-        prompt="The {animal} says", data={"animal": "duck"}, system_prompt=None
+    completion = await llm.call_single(  # run_prompt is deprecated
+        messages=messages,
     )
     assert completion.seconds_to_first_token == 0
     assert completion.seconds_to_last_token > 0
@@ -463,10 +470,13 @@ def accum(x) -> None:
         outputs.append(x)
 
     llm = anthropic_settings.get_llm()
-    completion = await llm.run_prompt(
-        prompt="The {animal} says",
-        data={"animal": "duck"},
-        system_prompt=None,
+    prompt = "The {animal} says"
+    data = {"animal": "duck"}
+    messages = [
+        Message(role="user", content=prompt.format(**data)),
+    ]
+    completion = await llm.call_single(  # run_prompt is deprecated
+        messages=messages,
         callbacks=[accum],
     )
     assert completion.seconds_to_first_token > 0
@@ -476,8 +486,8 @@ def accum(x) -> None:
     assert isinstance(completion.text, str)
     assert completion.cost > 0
 
-    completion = await llm.run_prompt(
-        prompt="The {animal} says", data={"animal": "duck"}, system_prompt=None
+    completion = await llm.call_single(  # run_prompt is deprecated
+        messages=messages,
     )
     assert completion.seconds_to_first_token == 0
     assert completion.seconds_to_last_token > 0
@@ -757,18 +767,50 @@ def test_hybrid_embedding(stub_data_dir: Path, vector_store: type[VectorStore])
 
 
 def test_custom_llm(stub_data_dir: Path) -> None:
-    from llmclient import Chunk
+    # ASK: Because acompletion_iter is @rate_limited in LiteLLMModel, we need to mock it here.
+    # I accept ideas on how to handle this better
+    def mock_rate_limited(func):
+        async def wrapper(self, *args, **kwargs):  # noqa: RUF029
+            async def mock_rate_limited_generator():
+                async for item in func(self, *args, **kwargs):
+                    yield item
+
+            return mock_rate_limited_generator()
+
+        return wrapper
 
     class StubLLMModel(LLMModel):
         name: str = "myllm"
 
-        async def acomplete(self, prompt: str) -> Chunk:  # noqa: ARG002
-            return Chunk(text="Echo", prompt_tokens=1, completion_tokens=1)
-
-        async def acomplete_iter(
-            self, prompt: str  # noqa: ARG002
-        ) -> AsyncIterable[Chunk]:
-            yield Chunk(text="Echo", prompt_tokens=1, completion_tokens=1)
+        async def acompletion(
+            self, messages: list[Message], **kwargs
+        ) -> list[LLMResult]:
+            # NOTE: Here we add kwargs to the function signature to make it compatible with the superclass
+            # and avoid #type: ignore[override]
+            # and we delete kwargs to avoid ARG002 (unused argument)
+            del kwargs
+            return [
+                LLMResult(
+                    model=self.name,
+                    text="Echo",
+                    prompt=messages,
+                    prompt_count=1,
+                    completion_count=1,
+                )
+            ]
+
+        @mock_rate_limited
+        async def acompletion_iter(
+            self, messages: list[Message], **kwargs
+        ) -> AsyncIterable[LLMResult]:
+            del kwargs
+            yield LLMResult(
+                model=self.name,
+                text="Echo",
+                prompt=messages,
+                prompt_count=1,
+                completion_count=1,
+            )
 
     docs = Docs()
     docs.add(