Awesome Diffusion Models for Drug Design

This is a repository summarizing the papers on the topic of Diffusion Models for Drug Design, and I also summarize the papers of ICML, ICLR, and NeurIPS.

• [ICML24] Molecules Generation
• [ICLR24] Molecules Generation

Contents

• De Novo Drug Design
  • Target-Agnostic Generation
  • Target-Aware Generation
  • Conformation Generation
• Controllable Molecular generation
• Molecular Optimization For Drug Design
  • Fragment-based drug discovery
  • Scaffold-based DMGs
• Structure-based Drug Design
• Drug-likeness and Evaluation metrics

Papers

De Novo Drug Design

Target-Agnostic Generation

• Equivariant Diffusion for Molecule Generation in 3D (EDM)
  Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling
  [2203.17003] [ICML 2022] [Github]
• Geometry-Complete Diffusion for 3D Molecule Generation and Optimization (GCDM)
  Alex Morehead, Jianlin Cheng
  arXiv:2302.04313 (2023)
• MDM: Molecular Diffusion Model for 3D Molecule Generation (MDM)
  Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong
  AAAI 2023
• Geometric Latent Diffusion Models for 3D Molecule Generation (GeoLDM)
  Minkai Xu, Alexander S Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec
  ICML 2023
• Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D [2023]
  Qiang, Bo, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, and Yanyan Lan. ICML (2023) | code
• Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation [2023] Huang, Han, Leilei Sun, Bowen Du, and Weifeng Lv.
  arXiv:2301.00427 (2023) | code | AAAI 2023
• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation (JODO)
  Han Huang, Leilei Sun, Bowen Du, Weifeng Lv
  arXiv:2305.12347 (2023)
• MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation (MiDi)
  Clement Vignac, Nagham Osman, Laura Toni, Pascal Frossard
  arXiv:2302.09048 (2023)

• Domain-Agnostic Molecular Generation with Self-feedback
  [Paper] [Code][ICLR24]
• SILVR: Guided Diffusion for Molecule Generation [2023]
  Runcie, Nicholas T., and Antonia SJS Mey.
  J. Chem. Inf. Model. (2023) | arXiv:2304.10905v1 | code
• De Novo Molecule Generation with Graph Latent Diffusion Model [2024]
  Wang, Conghao, Hiok Hian Ong, Shunsuke Chiba, and Jagath C. Rajapakse.
  ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2024)

Target-Aware Generation

• Structure-based Drug Design with Equivariant Diffusion Models
  Arne Schneuing¹, Yuanqi Du¹, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia
  arXiv:2210.13695 (2022) [Github]
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (TargetDiff)
  Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma
  ICLR 2023
• KGDiff: towards explainable target-aware molecule generation with knowledge guidance [2023] Hao Qian, Wenjing Huang, Shikui Tu, Lei Xu.
  Briefings in Bioinformatics. (2023) | code

Conformation Generation(append)

• Torsional Diffusion for Molecular Conformer Generation
  Bowen Jing, Gabriele Corso, Regina Barzilay, Tommi S. Jaakkola*
  ICLR Workshop 2022. [Paper] [Github]

• Predicting Molecular Conformation via Dynamic Graph Score Matching
  Shitong Luo, Chence Shi, Minkai Xu, Jian Tang*
  NeurIPS 2021. [Paper]

• GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation (GeoDiff)
  Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
  ICLR 2022

Controllable Molecular Generation

How to control：

• Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
  Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu
  ICML 2024 oral arXiv:2406.02066 | code
• Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
• (GeoLDM) Geometric Latent Diffusion Models for 3D Molecule Generation
  Minkai Xu, Alexander S Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec
  ICML 2023
• (MUDM) Training-free Multi-objective Diffusion Model for 3D Molecule Generation
  [Microsoft Research Asia, Microsoft Research AI4Science] Xu Han, Caihua Shan, Yifei Shen, Can Xu, Han Yang, Xiang Li, Dongsheng Li
  ICLR 2024 poster
• Retrieval-based Controllable Molecule Generation (RetMol)
  Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard Baraniuk, Anima Anandkumar
  ICLR-2023 | code
• ControlMol: Adding Substruture Control To Molecule Diffusion Models
  Qi Zhengyang, Liu Zijing, Zhang Jiying, Cao He, Li Yu
  Arxiv-2405.06659
• Diffusion-Driven Domain Adaptation for Generating 3D Molecules
  Authors: Haokai Hong, Wanyu Lin, Kay Chen Tan
  arXiv:2404.00962
• DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
  Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu
  arXiv:2403.13829
• Plug-And-Play Controllable Graph Generation With Diffusion Models
• A Property-Guided Diffusion Model For Generating Molecular Graphs [2024]
  Ma, Changsheng, Taicheng Guo, Qiang Yang, Xiuying Chen, Xin Gao, Shangsong Liang, Nitesh Chawla, and Xiangliang Zhang.
  ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2024)

Few shot Molecular Generation

• Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation [2024]
  Le, Tuan, Julian Cremer, Frank Noé, Djork-Arné Clevert, and Kristof Schütt.
  International Conference on Learning Representations (ICLR). (2024) | code
• Representing Molecules as Random Walks Over Interpretable Grammars
  [ICML 2024 Spotlight]
• Data-Efficient Molecular Generation with Hierarchical Textual Inversion
  [ICML 2024 poster]

Molecular Generation With Feedback

• Domain-Agnostic Molecular Generation with Chemical Feedback
  ICML24

Molecular Generation With Bayesian Flow

• Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks
  ICML24

Molecular Optimization For Drug Design

• Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties [2023]
  Guo, Siyuan, Jihong Guan, and Shuigeng Zhou.
  arXiv:2310.04463 (2023)
• A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets [2023]
  Huang, Lei.
  bioRxiv 2023.01.28.526011
• A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets [2024]
  Huang, L., Xu, T., Yu, Y. et al.
  Nat Commun 15, 2657 (2024) | code
• DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization [2023]
  Zixu Wang, Yangyang Chen*, Xiucai Ye.
  chemrxiv-2023-ltr9v-v2. (2023) | code

Fragment-based drug discovery

• ReBADD-SE: Multi-objective molecular optimization using SELFIES fragment and off-policy self-critical sequence training [2023]
  Choi, Jonghwan, Sangmin Seo, Seungyeon Choi, Shengmin Piao, Chihyun Park, Sung Jin Ryu, Byung Ju Kim, and Sanghyun Park.
  Computers in Biology and Medicine 157 (2023) | code
• Autoregressive fragment-based diffusion for pocket-aware ligand design [2023]
  Ghorbani, Mahdi, Leo Gendelev, Paul Beroza, and Michael Keiser.
  NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. (2023) | code

Scaffold-based DMGs

• DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion [2023]
  Xie, Junjie, Sheng Chen, Jinping Lei, and Yuedong Yang.
  bioRxiv (2023)
• DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping [2023]
  Torge, Jos, Charles Harris, Simon V. Mathis, and Pietro Lió.
  ICML(2023) | code

Structure-based Drug Design

• A unified conditional diffusion framework for dual protein targets based bioactive molecule generation [2024]
  Huang, Lei, Zheng Yuan, Huihui Yan, Rong Sheng, Linjing Liu, Fuzhou Wang, Weidun Xie et al.
  IEEE Transactions on Artificial Intelligence (2024) | arXiv:2306.13957 (2023)
• AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design [2024]
  Li, Xinze, Penglei Wang, Tianfan Fu, Wenhao Gao, Chengtao Li, Leilei Shi, and Junhong Liu.
  arXiv:2404.02003 (2024)
• MolSnapper: Conditioning Diffusion for Structure Based Drug Design [2024]
  Ziv, Yael, Brian Marsden, and Charlotte Deane.
  bioRxiv (2024) | code
• A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets [2024]
  Huang, L., Xu, T., Yu, Y. et al.
  Nat Commun 15, 2657 (2024) | code
• KGDiff: towards explainable target-aware molecule generation with knowledge guidance [2023]
  Hao Qian, Wenjing Huang, Shikui Tu, Lei Xu.
  Briefings in Bioinformatics. (2023) | code
• Geometric Deep Learning for Structure-Based Ligand Design [2023]
  Alexander S. Powers, Helen H. Yu, Patricia Suriana, Rohan V. Koodli, Tianyu Lu, Joseph M. Paggi, and Ron O. Dror.
  ACS Cent. Sci. (2023)
• Autoregressive fragment-based diffusion for pocket-aware ligand design [2023]
  Ghorbani, Mahdi, Leo Gendelev, Paul Beroza, and Michael Keiser.
  NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. (2023) | code
• DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins [2023]
  Huang, Lei, Zheng Yuan, Huihui Yan, Rong Sheng, Linjing Liu, Fuzhou Wang, Weidun Xie et al.
  arXiv:2306.13957 (2023)
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [2023] Guan, Jiaqi, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma.
  The Eleventh International Conference on Learning Representations. (2023) | code
• Structure-based Drug Design with Equivariant Diffusion Models [2023]
  Schneuing, A., Du, Y., Harris, C., Jamasb, A., Igashov, I., Du, W., ... & Correia, B.
  arXiv:2210.13695 (2022) | code
• DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [2023] Guan, Jiaqi, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, and Quanquan Gu.
  ICML (2023) | code
• Binding-Adaptive Diffusion Models for Structure-Based Drug Design [2024] Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang. AAAI 2024 (2024) | code
• Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models [2023]
  Chen, Ziqi, Bo Peng, Srinivasan Parthasarathy, and Xia Ning
  arXiv:2308.11890 (2023)

Drug-likeness and Evaluation metrics

QED

Target-Agnostic Generation & Target-Aware Generation

quantitative estimation of drug-likeness

• Quantifying the chemical beauty of drugs [2012]
  Bickerton, G., Paolini, G., Besnard, J. et al.
  Nature Chem 4, 90–98 (2012) | code

SAscore

Target-Aware Generation

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
J Cheminform 1, 8 (2009) | code

RAscore

Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning
Chemical Science 12.9 (2021) | code

Evaluation metrics

• Spacial Score – A Comprehensive Topological Indicator for Small Molecule Complexity [2023]
  Krzyzanowski, Adrian, Axel Pahl, Michael Grigalunas, and Herbert Waldmann.
  J. Med. Chem. (2023) | chemrxiv-2023-nd1ll | code
• An automated scoring function to facilitate and standardize evaluation of goal-directedgenerative models for de novo molecular design [2023]
  Thomas, Morgan, Noel M. O'Boyle, Andreas Bender, and Chris De Graaf.
  chemrxiv-2023-c4867 | code
• FCD : Fréchet ChemNet Distance
  Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
  Preuer, Kristina, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer.
  J. Chem. Inf. Model. 2018, 58, 9, 1736–1741 | code
• Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models [2022] Moret, M., Grisoni, F., Katzberger, P. and Schneider, G.
  J. Chem. Inf. Model. 2022, 62, 5, 1199–1206 | code
• AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading (Vina AutoDock)
  Oleg Trott, Arthur J. Olson
  JCC 2010

# Some Other Tasks

Inverse Molecular Design

• Equivariant Energy-Guided SDE for Inverse Molecular Design
  Fan Bao¹, Min Zhao¹, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
  arXiv 2022. [Paper]
• Inverse Molecular Design with Multi-Conditional Diffusion Guidance [2024] Liu, Gang, Jiaxin Xu, Tengfei Luo, and Meng Jiang. arXiv:2401.13858 (2024) | code
• STRIDE: Structure-guided Generation for Inverse Design of Molecules [2023]
  Zaman, Shehtab, Denis Akhiyarov, Mauricio Araya-Polo, and Kenneth Chiu.
  NeurIPS 2023 AI for Science Workshop. (2023)
• Guided Diffusion for Inverse Molecular Design [2023]
  Weiss, Tomer, Luca Cosmo, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Alex M. Bronstein, and Renana Gershoni-Poranne.
  Nat Comput Sci (2023) | chemrxiv-2023-z8ltp | code

Text to Molecular

• 3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs [2024]
  Huaisheng Zhu, Teng Xiao, Vasant G Honavar.
  arXiv:2403.07179. (2024) | code
• Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization [2024]
  Zhang, Kaiwei, Yange Lin, Guangcheng Wu, Yuxiang Ren, Xuecang Zhang, Bo Wang, and Xiao-Yu Zhang.
  Research Square (2024)
• Guided Diffusion for molecular generation with interaction prompt [2023]
  Wu Song, Peng Wu, Huabin Du, Yingchao Yan, Chen Bai
  bioRxiv (2023) | data

#To check later

• Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
  Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia
  arXiv 2022. [Paper]
• Diffusion-based Molecule Generation with Informative Prior Bridges
  Lemeng Wu¹, Chengyue Gong¹, Xingchao Liu, Mao Ye, Qiang Liu*
  NeurIPS 2022. [Paper]
• Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design [2024]
  Lin, Jie, Mingyuan Xu, and Hongming Chen.
  chemrxiv-2024-km0h1 (2024)
• A unified conditional diffusion framework for dual protein targets based bioactive molecule generation [2024]
  Huang, Lei, Zheng Yuan, Huihui Yan, Rong Sheng, Linjing Liu, Fuzhou Wang, Weidun Xie et al.
  IEEE Transactions on Artificial Intelligence (2024) | arXiv:2306.13957 (2023)
• Equivariant 3D-conditional diffusion model for molecular linker design [2024]
  Igashov, I., Stärk, H., Vignac, C. et al.
  Nat Mach Intell (2024) | code
• Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration [2024] Lin, Haitao, Yufei Huang, Odin Zhang, Yunfan Liu, Lirong Wu, Siyuan Li, Zhiyuan Chen, and Stan Z. Li. Advances in Neural Information Processing Systems 36 (2024)
• Field-based Molecule Generation [2024] Dumitrescu, Alexandru, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, and Harri Lähdesmäki.
  arXiv:2402.15864 (2024)
• LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion [2023]
  Guan, Jiaqi, Xingang Peng, PeiQi Jiang, Yunan Luo, Jian Peng, and Jianzhu Ma
  NeurIPS 2023. (2023) | code
• MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation [2023]
  Peng, Xingang, Jiaqi Guan, Qiang Liu, and Jianzhu Ma.
  ICML (2023) | code
• Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Deep Neural Networks with Application to Deep Eutectic Solvents [2023]
  Luu, Rachel K., Marcin Wysokowski, and Markus J. Buehler.
  arXiv:2304.12400v1 | code