remove langchain dependency

exmol/exmol.py

@@ -27,9 +27,8 @@
 from rdkit.Chem.Draw import MolToImage as mol2img, DrawMorganBit  # type: ignore
 from rdkit.Chem import rdchem  # type: ignore
 from rdkit.DataStructs.cDataStructs import BulkTanimotoSimilarity, TanimotoSimilarity  # type: ignore
-import langchain.llms as llms
-import langchain.prompts as prompts
 
+import openai
 from . import stoned
 from .plot_utils import _mol_images, _image_scatter, _bit2atoms
 from .data import *
@@ -392,6 +391,7 @@ def _check_alphabet_consistency(
     alphabet_symbols = _alphabet_to_elements(set(alphabet_symbols))
     # find all elements in smiles (Upper alpha or upper alpha followed by lower alpha)
     smiles_symbols = set(re.findall(r"[A-Z][a-z]?", smiles))
+
     if check and not smiles_symbols.issubset(alphabet_symbols):
         # show which symbols are not in alphabet
         raise ValueError(
@@ -1410,7 +1410,7 @@ def merge_text_explains(
 def text_explain_generate(
     text_explanations: List[Tuple[str, float]],
     property_name: str,
-    llm: Optional[llms.BaseLLM] = None,
+    llm_model: str = 'gpt-4',
     single: bool = True,
 ) -> str:
     """Insert text explanations into template, and generate explanation.
@@ -1430,14 +1430,19 @@ def text_explain_generate(
             for x in text_explanations
         ]
     )
-    prompt_template = prompts.PromptTemplate(
-        input_variables=["property", "text"],
-        template=_single_prompt if single else _multi_prompt,
-    )
+
+    prompt_template = _single_prompt if single else _multi_prompt
     prompt = prompt_template.format(property=property_name, text=text)
-    if llm is None:
-        llm = llms.OpenAI(temperature=0.05)
-    return llm(prompt)
+
+    messages = [{"role": "system", "content": "Your goal is to explain which molecular features are important to its properties based on the given text."},
+        {"role": "user", "content": prompt}]
+    response = openai.ChatCompletion.create(
+        model=llm_model,
+        messages=messages,
+        temperature=0.05,
+    )
+
+    return response.choices[0].message["content"]
 
 
 def text_explain(

paper2_LIME/RF-lime.ipynb

@@ -30,15 +30,14 @@
     "import numpy as np\n",
     "import mordred, mordred.descriptors\n",
     "from mordred import HydrogenBond, Polarizability\n",
-    "from mordred import SLogP, AcidBase, BertzCT, Aromatic, BondCount, AtomCount\n",
+    "from mordred import SLogP, AcidBase, Aromatic, BondCount, AtomCount\n",
     "from mordred import Calculator\n",
     "\n",
     "import exmol as exmol\n",
     "from rdkit.Chem.Draw import rdDepictor\n",
     "import os\n",
     "from sklearn.ensemble import RandomForestRegressor\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import roc_auc_score, plot_roc_curve\n",
     "\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
     "rdDepictor.SetPreferCoordGen(True)\n",
@@ -50,6 +49,9 @@
     "soldata = pd.read_csv(\n",
     "    \"https://github.com/whitead/dmol-book/raw/main/data/curated-solubility-dataset.csv\"\n",
     ")\n",
+    "#drop smile with containing 'P'\n",
+    "soldata = soldata[soldata[\"SMILES\"].str.contains(\"P\") == False]\n",
+    "\n",
     "features_start_at = list(soldata.columns).index(\"MolWt\")"
    ]
   },
@@ -97,7 +99,8 @@
    "outputs": [],
    "source": [
     "raw_features = np.array(raw_features)\n",
-    "labels = soldata[\"Solubility\"]"
+    "labels = soldata[\"Solubility\"]\n",
+    "print(len(labels)==len(molecules))"
    ]
   },
   {
@@ -197,7 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "smi = soldata.SMILES[1500]\n",
+    "smi = soldata.SMILES[150]\n",
     "stoned_kwargs = {\n",
     "    \"num_samples\": 2000,\n",
     "    \"alphabet\": exmol.get_basic_alphabet(),\n",

paper2_LIME/Solubility-RNN.ipynb

@@ -22,21 +22,15 @@
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib.patches import Rectangle, FancyBboxPatch\n",
-    "from matplotlib.offsetbox import AnnotationBbox\n",
-    "import seaborn as sns\n",
-    "import skunk\n",
     "import matplotlib as mpl\n",
     "import numpy as np\n",
     "import tensorflow as tf\n",
     "import selfies as sf\n",
     "import exmol\n",
     "from dataclasses import dataclass\n",
     "from rdkit.Chem.Draw import rdDepictor, MolsToGridImage\n",
-    "from rdkit.Chem import MolFromSmiles, MACCSkeys\n",
+    "from rdkit.Chem import MolFromSmiles\n",
     "import random\n",
-    "\n",
-    "\n",
     "rdDepictor.SetPreferCoordGen(True)\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.font_manager as font_manager\n",
@@ -66,6 +60,7 @@
     "soldata = pd.read_csv(\n",
     "    \"https://github.com/whitead/dmol-book/raw/main/data/curated-solubility-dataset.csv\"\n",
     ")\n",
+    "\n",
     "features_start_at = list(soldata.columns).index(\"MolWt\")\n",
     "np.random.seed(0)\n",
     "random.seed(0)"

paper2_LIME/requirements.txt

@@ -4,3 +4,4 @@ jupyter
 seaborn
 pandas
 tensorflow>=2.4
+