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https://github.com/wudangt/awesome-molecular-modeling-and-drug-discovery

A curated list of awesome Molecular Modeling And Drug Discovery 🔥
https://github.com/wudangt/awesome-molecular-modeling-and-drug-discovery

List: awesome-molecular-modeling-and-drug-discovery

alphafold alphafold2 bioinformatics drug-discovery molecular-docking molecular-evolution molecular-generation molecular-modeling protein-engineering protein-sequence protein-structure structural-biology

Last synced: 3 months ago
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A curated list of awesome Molecular Modeling And Drug Discovery 🔥

Host: GitHub
URL: https://github.com/wudangt/awesome-molecular-modeling-and-drug-discovery
Owner: wudangt
Created: 2022-06-29T05:35:24.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-07-21T04:56:23.000Z (over 2 years ago)
Last Synced: 2024-05-22T05:03:22.149Z (6 months ago)
Topics: alphafold, alphafold2, bioinformatics, drug-discovery, molecular-docking, molecular-evolution, molecular-generation, molecular-modeling, protein-engineering, protein-sequence, protein-structure, structural-biology
Homepage:
Size: 51.8 KB
Stars: 10
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # 🔥 awesome-molecular-modeling-and-drug-discovery

[![Awesome](https://cdn.combinatronics.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

> A curated list of awesome Molecular Modeling And Drug Discovery

## Table of Contents

- [Table of Contents](#table-of-contents)

- [Resources](#resources)

	- [Research articles](#research-articles)

		- [Year 2022](#year-2022)

		- [Year 2021](#year-2021)

		- [Year 2020](#year-2020)

		- [Year 2019](#year-2019)

		- [Year 2018](#year-2018)

		- [Year 2017](#year-2017)

	- [Community](#community)

	- [conference](#conference)

	- [Tutorials](#tutorials)

	- [Jobs](#jobs)

	- [Other software and resources](#other-software-and-resources)

		- [Platforms](#platforms)

		- [DevTools](#devtools)

## Resources 

### Research articles

#### Year 2022

1. **Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking**  

Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, etc, ... ICLR 2022 [paper](https://openreview.net/forum?id=GQjaI9mLet)

2. **Harnessing protein folding neural networks for peptide–protein docking**   

Tomer Tsaban, Julia K. Varga, Orly Avraham, Ziv Ben-Aharon, etc, ... Nature 2022 [paper](https://www.nature.com/articles/s41467-021-27838-9)

3. **Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design**  

Wenhao Gao, Rocío Mercado, Connor W. Coley  Arxiv 2021 [paper](https://arxiv.org/abs/2110.06389)

4. **Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations**  

Keir Adams, Lagnajit Pattanaik, Connor Coley ICLR [paper](https://arxiv.org/abs/2110.04383)

5. **Crystal Diffusion Variational Autoencoder for Periodic Material Generation**    

Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola ICLR 2022 [paper](https://arxiv.org/abs/2110.06197)

6. **AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor**  

Feng Ren, Xiao Ding, Min Zheng, Mikhail Korzinkin, Xin Cai ArXiv 2022 [paper](https://arxiv.org/abs/2201.09647)

7. **An RNA-based theory of natural universal computation**   

HessameddinAkhlaghpour Journal of Theoretical Biology 2022 [paper](https://www.sciencedirect.com/science/article/pii/S0022519321004045?dgcid=author)

8.  **GeneDisco: A Benchmark for Experimental Design in Drug Discovery**   

Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, etc, ... ICLR 2022 [paper](https://openreview.net/forum?id=-w2oomO6qgc)

9. **ChemicalX: A Deep Learning Library for Drug Pair Scoring**   

Rozemberczki, Benedek and Hoyt, Charles Tapley and Gogleva, etc, ... KDD 2022 [paper](https://arxiv.org/abs/2202.05240)

10. **Enhanced sampling methods for molecular dynamics simulations**   

Jérôme Hénin, Tony Lelièvre, Michael R. Shirts, Omar Valsson, Lucie Delemotte ArXiv 2022 [paper](https://arxiv.org/abs/2202.04164)

11. **GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation**   

Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang ICLR 2022 [paper](https://arxiv.org/abs/2203.02923)

12. **Deep sharpening of topological features for de novo protein design**   

Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, etc, ...  ICLR Workshop 2022 [paper](https://openreview.net/forum?id=DwN81YIXGQP)

13. **Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models**   

Namrata Anand and Tudor Achim ArXiv 2022 [paper](https://nanand2.github.io/proteins/)

14. **ColabFold: making protein folding accessible to all**   

Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, etc, ... Nature [paper](https://www.nature.com/articles/s41592-022-01488-1)

15. **Design of protein-binding proteins from the target structure alone**  

Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, etc, ... Nature [paper](https://www.nature.com/articles/s41586-022-04654-9)

16. **EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction**   

Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi Jaakkola ICML 2022 [paper](https://arxiv.org/abs/2202.05146)

17. **RITA: a Study on Scaling Up Generative Protein Sequence Models**   

Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, Debora Marks  ArXiv 2022 [paper](https://arxiv.org/abs/2205.05789)

18. **QMugs, quantum mechanical properties of drug-like molecules**   

Clemens Isert, Kenneth Atz, José Jiménez-Luna & Gisbert Schneider Nature 2022 [paper](https://www.nature.com/articles/s41597-022-01390-7)

19. **Asymmetric Proton Transfer Catalysis by Stereocomplementary Old Yellow Enzymes for C═C Bond Isomerization Reaction**   

Marina S. Robescu, Laura Cendron, Arianna Bacchin, Karla Wagner, Tamara Reiter, etc, ... Chemical Reviews 2021 [paper](https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00022#)

#### Year 2021

1. **Geometric Deep Learning on Molecular Representations**  

Kenneth Atz, Francesca Grisoni, Gisbert Schneider Nature 2021 [paper](https://www.nature.com/articles/s42256-021-00418-8)

2. **Highly accurate protein structure prediction with AlphaFold**  

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, etc, ... Nature 2021 [paper](https://www.nature.com/articles/s41586-021-03819-2)

3. **Accurate prediction of protein structures and interactions using a three-track neural network**   

MINKYUNG BAEK, DIMAIO, ANISHCHENKO, DAUPARAS, etc, ... Science 2021 [paper](https://www.science.org/doi/10.1126/science.abj8754)

5. **Learning from Protein Structure with Geometric Vector Perceptrons**  

Bowen Jing, Stephan Eismann∗, Patricia Suriana, Raphael J.L. T, etc, ... ICLR 2021 [paper](https://arxiv.org/pdf/2009.01411.pdf)

6. **An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming**  

Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, etc, ...  PMLR 2021 [paper](https://proceedings.mlr.press/v139/xu21f.html)

7. **Learning Gradient Fields for Molecular Conformation Generation**  

Chence Shi, Shitong Luo, Minkai Xu, Jian Tang ICML 2021 [paper](https://arxiv.org/abs/2105.03902)

8. **Gaussian Process Regression for Materials and Molecules**   

Volker L. Deringer*, Albert P. Bartók*, Noam Bernstein, etc, ... Chemical Reviews 2021 [paper](https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00022#)

9. **GemNet: Universal Directional Graph Neural Networks for Molecules**   

Johannes Gasteiger, Florian Becker, Stephan Günnemann, etc, ... NeurIPS 2021 [paper](https://arxiv.org/pdf/2106.08903.pdf)

10. **Do Large Scale Molecular Language Representations Capture Important Structural Information?**   

Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, etc, ... Arxiv 2021 [paper](https://arxiv.org/abs/2106.09553)

11. **Using AlphaFold to predict the impact of single mutations on protein stability and function**   

Marina A. Pak1, Karina A. Markhieva2, Mariia S. Novikova, etc, ... BioRxiv  2021 [paper](https://www.biorxiv.org/content/10.1101/2021.09.19.460937v1)

12. **Disease variant prediction with deep generative models of evolutionary data**   

Jonathan Frazer, Pascal Notin, Mafalda Dias, Aidan Gomez, etc, ... Nature 2021 [paper](https://www.nature.com/articles/s41586-021-04043-8)

#### Year 2020

1. **SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks**  

Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling NeurIPS 2020 [paper](https://proceedings.neurips.cc//paper/2020/hash/15231a7ce4ba789d13b722cc5c955834-Abstract.html)

#### Year 2019

#### Year 2018

#### Year 2017

1. **Structure in neural population recordings: an expected byproduct of simpler phenomena?**   

Gamaleldin F Elsayed & John P Cunningham  Nature 2022 [paper](https://www.nature.com/articles/nn.4617)

### Community

1. **M2D2: Molecular Modeling And Drug Discovery** [Link](https://valence-discovery.github.io/M2D2-meetings/index.html)

2. **OpenFold：Democratizing AI for Biology** [Link](https://openfold.io/#whatis)

3. **LoGaG: Learning on Graphs and Geometry Reading Group** [Link](https://hannes-stark.com/logag-reading-group)

### Conference

1. **AI Cures Drug Discovery Conference** [Link](https://www.aicures.mit.edu/drugdiscoveryconference)

### Tutorials

1. **DeepMind's AlphaFold 2 Explained! AI Breakthrough in Protein Folding! What we know (& what we don't)** [Video](https://www.youtube.com/watch?v=B9PL__gVxLI&ab_channel=YannicKilcher)

2. **AlphaFold and the Grand Challenge to solve protein folding** [Video](https://www.youtube.com/watch?v=nGVFbPKrRWQ&t=3s&ab_channel=ArxivInsights)

3. **Michelle Gill - Artificial Intelligence Driven Drug Discovery** [Video](https://www.youtube.com/watch?v=U_rGTUyMkxg&ab_channel=LanderAnalytics)

4. **AI for Drug Design - Lecture 16 - Deep Learning in the Life Sciences (Spring 2021)** [Video](https://www.youtube.com/watch?v=AHVJv5RNqKs&ab_channel=ManolisKellis)

5. **Deep Learning for Drug Discovery** [Video](https://www.youtube.com/watch?v=Xf2uI4S9IMo&ab_channel=BayesGroup.ru)

6. **An Introduction to Computational Drug Discovery** [Video](https://www.youtube.com/watch?v=RL25hgfLd8Q&ab_channel=DataProfessor)

7. **Data Science for Computational Drug Discovery using Python (Part 1)** [Video](https://www.youtube.com/watch?v=VXFFHHoE1wk)

8. **Data Science for Computational Drug Discovery using Python (Part 2 with PyCaret)** [Video](https://www.youtube.com/watch?v=RGfeGRt32Dk&t=0s&ab_channel=DataProfessor)

9. **DeepMind solves protein folding | AlphaFold 2** [Video](https://www.youtube.com/watch?v=W7wJDJ56c88&ab_channel=LexFridman)

10. **FS-Mol: Bringing Deep Learning to Early-Stage Drug Discovery** [Blog](https://www.microsoft.com/en-us/research/blog/fs-mol-bringing-deep-learning-to-early-stage-drug-discovery/?OCID=msr_blog_FSMol_NeurIPS_tw)

11. **Open Source Initiatives to get you Started with AI in Drug Discovery** [Video](https://www.youtube.com/watch?v=kBL3tB6cVlw)

12. **Bayesian Modelling of Synergistic Drug Combination Effects in Cancer Using Gaussian Processes** [Video](https://www.youtube.com/watch?v=DHAOCNUhqeI)

13. **Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric ML** [Video](https://www.youtube.com/watch?v=r_ZTOoGxFC0)

14. **The hype on AlphaFold keeps growing with this new preprint**  [Blog](https://towardsdatascience.com/the-hype-on-alphafold-keeps-growing-with-this-new-preprint-a8c1f21d15c8)

15. **What's next for AlphaFold and the AI protein-folding revolution**  [Blog](https://www.nature.com/articles/d41586-022-00997-5)

### Jobs

1. **Somorphic Labs — Current Job Openings** [Apply](https://www.isomorphiclabs.com/join)

### Other software and resources

#### Platforms

1. **ColabFold: making protein folding accessible to all** [Link](https://www.nature.com/articles/s41592-022-01488-1)

2. **TheBioProgrammingLab：combines mammalian synthetic biology with de novo protein design** [Link](https://chenlab.org/)

#### DevTools

1. **STK: a Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design, and the creation of molecular, and molecular property, databases** [Link](https://stk.readthedocs.io/en/stable/index.html#)

2. **exmol: Explainer for black box models that predict molecule properties** [Link](https://github.com/ur-whitelab/exmol)

3. **ChemicalX: A Deep Learning Library for Drug Pair Scoring** [Link](https://arxiv.org/abs/2202.05240)

4. **GeneDisco: A Benchmark for Experimental Design in Drug Discovery** [Link](https://arxiv.org/abs/2110.11875)