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Molecular-Optimization via Generative AI and Deep Learning

contributing-image

papers related to Generative AI and Deep Learning for Molecular Optimization.

A visual presentation of the process of molecular optimization Molecular Optimization

Refs
Beck, Hartmut, Tobias Thaler, Daniel Meibom, Mark Meininghaus, Hannah Jörißen, Lisa Dietz, Carsten Terjung et al. "Potent and selective human prostaglandin F (FP) receptor antagonist (BAY-6672) for the treatment of idiopathic pulmonary fibrosis (IPF)." Journal of medicinal chemistry 63, no. 20 (2020): 11639-11662.

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RNN-based LSTM-based Autoregressive-models Transformer-based
VAE-based GAN-based Flow-based
Score-Based Energy-based Diffusion-based
RL-based Active Learning-based

Reviews

  • Computer-aided multi-objective optimization in small molecule discovery [2023]
    Fromer, J. C., & Coley, C. W.
    Patterns 4.2 (2023)

Datasets and Benchmarks

Datasets

DrugBank

https://go.drugbank.com/

ChEMBL

https://www.ebi.ac.uk/chembl/

PubChem

https://pubchem.ncbi.nlm.nih.gov/

Benchmarks

  • Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization [2022]
    Gao, Wenhao, Tianfan Fu, Jimeng Sun, and Connor W. Coley.
    Paper | code

Deep Learning-Molecular Optimization

RNN-based

LSTM-based

Autoregressive-models

  • FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization [2023]
    Jieyu Jin, Dong Wang, Guqin Shi, Jingxiao Bao, Jike Wang, Haotian Zhang, Peichen Pan, Dan Li, Xiaojun Yao, Huanxiang Liu, Tingjun Hou, and Yu Kang
    J. Med. Chem. (2023) | code

  • Domain-Agnostic Molecular Generation with Self-feedback [2023]
    Yin Fang, Ningyu Zhang, Zhuo Chen, Xiaohui Fan, Huajun Chen
    Paper | code

Transformer-based

  • Transformer-based deep learning method for optimizing ADMET properties of lead compounds [2023]
    Yang, Lijuan, Chao Jin, Guanghui Yang, Zhitong Bing, Liang Huang, Yuzhen Niu, and Lei Yang.
    Physical Chemistry Chemical Physics 25.3 (2023)

  • Molecular optimization by capturing chemist’s intuition using deep neural networks [2021]
    He, Jiazhen, Huifang You, Emil Sandström, Eva Nittinger, Esben Jannik Bjerrum, Christian Tyrchan, Werngard Czechtizky, and Ola Engkvist.
    J Cheminform 13, 26 (2021) | code

  • Transformer-based molecular optimization beyond matched molecular pairs [2022]
    He, Jiazhen, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, and Ola Engkvist.
    J Cheminform 14, 18 (2022) | code

  • Transformer Neural Network-Based Molecular Optimization Using General Transformations [2021]
    He, Jiazhen, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, and Ola Engkvist.
    Paper | code

VAE-based

GAN-based

Flow-based

  • FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization [2023]
    Jieyu Jin, Dong Wang, Guqin Shi, Jingxiao Bao, Jike Wang, Haotian Zhang, Peichen Pan, Dan Li, Xiaojun Yao, Huanxiang Liu, Tingjun Hou, and Yu Kang
    J. Med. Chem. (2023) | code

Diffusion-based

  • A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets [2023]
    Huang, Lei.
    bioRxiv 2023.01.28.526011

RL-based

  • ReBADD-SE: Multi-objective molecular optimisation 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.
    Paper | code

  • Gargoyles: An Open Source Graph-based molecular optimization method based on Deep Reinforcement Learning [2023]
    Erikawa, D., Yasuo, N., & Sekijima, M.
    Paper | code

  • Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design [2019]
    Ståhl, Niclas, Goran Falkman, Alexander Karlsson, Gunnar Mathiason, and Jonas Bostrom.
    Paper | code

Active Learning-based

  • Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks [2023]
    Isaac Filella-Merce, Alexis Molina, Marek Orzechowski, Lucía Díaz, Yang Ming Zhu, Julia Vilalta Mor, Laura Malo, Ajay S Yekkirala, Soumya Ray, Victor Guallar
    Paper | code

  • Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling [2023]
    Gusev, Filipp, Evgeny Gutkin, Maria G. Kurnikova, and Olexandr Isayev.
    Paper

Text-driven Molecular Optimization

  • Domain-Agnostic Molecular Generation with Self-feedback [2023]
    Yin Fang, Ningyu Zhang, Zhuo Chen, Xiaohui Fan, Huajun Chen
    Paper | code

Fragment-based Molecular Optimization

Scaffold-based Molecular Optimization

  • Differentiable Scaffolding Tree for Molecule Optimization [2022]
    Fu, Tianfan, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley, and Jimeng Sun.
    Paper | code

Fragment-based Molecular Optimization

  • FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization [2023]
    Jieyu Jin, Dong Wang, Guqin Shi, Jingxiao Bao, Jike Wang, Haotian Zhang, Peichen Pan, Dan Li, Xiaojun Yao, Huanxiang Liu, Tingjun Hou, and Yu Kang
    J. Med. Chem. (2023) | code

  • ReBADD-SE: Multi-objective molecular optimisation 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.
    Paper | code

  • A deep generative model for molecule optimization via one fragment modification [2021]
    Chen, Ziqi, Martin Renqiang Min, Srinivasan Parthasarathy, and Xia Ning.
    Paper | code

  • Fragment-Based Sequential Translation for Molecular Optimization [2021]
    Chen, Benson, Xiang Fu, Regina Barzilay, and Tommi Jaakkola.
    Paper

Multi-Objective Molecular Optimization

  • Human-in-the-loop assisted de novo molecular design [2022]
    Sundin, Iiris, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, and Ola Engkvist.
    Paper | code

  • Molecule optimization via multi-objective evolutionary in implicit chemical space [2022]
    Xia, Xin, Yansen Su, Chunhou Zheng, and Xiangxiang Zeng.
    Paper

  • Efficient multi-objective molecular optimization in a continuous latent spac [2019]
    Winter, Robin, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert.
    Paper | code