Awesome

awesome-drug-discovery

A collection of drug discovery, classification and representation learning papers with deep learning.

Tutorial

• torch_geometric for chemoinformatics

Survey

• Applications of machine learning in drug discovery and development (Nature Reviews drug discovery 2019)
  • Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer & Shanrong Zhao
  • [Paper(nature)]
  • [Paper(sci-hub)]
• Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics (bioRxiv 2019)
  • Benoit Playe, Véronique Stoven
  • [Paper]
  • [Python Reference]
• Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (Chemical Science 2019)
  • Andreas Mayr et.
  • [Paper]
• PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction (Arxiv 2018)
  • Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, Martin Ester
  • [Paper]
  • [Python Reference]

Tradintional Machine Learning

• A Bayesian machine learning approach for drug target identification using diverse data types (Nature Communications 2019)
  • Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Martin Stogniew, Joshua E. Allen, Paraskevi Giannakakou & Olivier Elemento
  • [Paper]
• Drug repositioning based on bounded nuclear norm regularization (ISMB/ECCB 2019)
  • Mengyun Yang, Huimin Luo, Yaohang Li and Jianxin Wang
  • [Paper]
  • [Matlab Reference]

Deep Learning

• MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins (RECOMB 2020)
  • Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, Jianyang Zeng
  • [Paper]
  • [Python Reference]
• Evaluating Protein Transfer Learning with TAPE (NIPS 2019)
  • Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song
  • [Paper]
  • [Python Reference(pytorch)]
  • [Python Reference(tensorflow)]
• Predicting Drug−Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (ACS 2019)
  • Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham and Woo Youn Kim
  • [Paper]
  • [Python Reference]
• DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network (ACS 2019)
  • Xiuming Li, Xin Yan, Qiong Gu, Huihao Zhou, Di Wu and Jun Xu
  • [Paper]
  • [Python Reference]
• DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (PLOS 2019)
  • Ingoo LeeID, Jongsoo Keum, Hojung NamID
  • [Paper]
  • [Python Reference]
• A Domain Knowledge Constraint Variantional Model for Drug Discovery (AAAI 2020 preprint review)
• DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction (AAAI 2020 preprint review)
• DAEM: Deep Attribute Embedding based Multi-Task Learning for Predicting Adverse Drug-Drug Interaction (AAAI 2020 preprint review)
• Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism (Journal of Medicinal Chemistry 2019)
  • Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang and Mingyue Zheng
  • [Paper]
  • [Python Reference]
• GraphDTA: prediction of drug–target binding affinity using graph convolutional networks (BioArxiv 2019)
  • Thin Nguyen, Hang Le, Svetha Venkatesh
  • [Paper]
  • [Python Reference]
• Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction (2019)
  • Bonggun Shin
  • [Paper]
• Multifaceted protein–protein interaction prediction based on Siamese residual RCNN (ISMB/ECCB 2019)
  • Muhao Chen1, Chelsea J.-T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo and Wei Wang
  • [Paper]
  • [Python Reference]
• Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors (Arxiv 2019)
  • Huy Ngoc Pham, Trung Hoang Le
  • [Paper]
  • [Python Reference]
• LEARNING PROTEIN SEQUENCE EMBEDDINGS USING INFORMATION FROM STRUCTURE (ICLR 2019)
  • Tristan Bepler, Bonnie Berger
  • [Paper]
  • [Python Reference]
• NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions (Bioinformatics 2019)
  • Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng
  • [Paper]
  • [Python Reference]
• DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks (Bioinformatics 2019)
  • Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen
  • [Paper]
  • [Python Reference]
• WideDTA: prediction of drug-target binding affinity (Arxiv 2019)
  • Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
  • [Paper]
  • [Python Reference]
• Predicting Drug Protein Interaction using Quasi-Visual Question Answering System (bioRxiv 2019)
  • Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang
  • [Paper]
• Drug2Vec: Knowledge-aware Feature-driven Method for Drug Representation Learning (BIBM 2018)
  • Ying Shen, Kaiqi Yuan, Yaliang Li, Buzhou Tang, Min Yang, Nan Du, Kai Lei
  • [Paper]
• Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (ACS 2018)
  • Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud
  • [Paper]
  • [Python Reference]
• Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences (Bioinformatics 2018)
  • Masashi Tsubaki, Kentaro Tomii, Jun Sese
  • [Paper]
  • [Python Reference]
• Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (KDD 2018)
  • Shahar Harel, Kira Radinsky
  • [Paper]
  • [Python Reference]
• DeepDTA: deep drug–target binding affinity prediction (Bioinformatics 2018)
  • Hakime Öztürk, Arzucan Özgür, Elif Ozkirimli
  • [Paper]
  • [Python Reference]
• Interpretable Drug Target Prediction Using Deep Neural Representation (IJCAI 2018)
  • Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, Ping Zhang
  • [Paper]
• Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (bioRxiv 2018)
  • Wen Torng, Russ B. Altman
  • [Paper]
• Chemi-Net: A molecular graph convolutional network for accurate drug property prediction (Arxiv 2018)
  • Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois, Jie Fan
  • [Paper]
• CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations (CoRR 2018)
  • Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok N. Choudhary, Ankit Agrawal
  • [Paper]
  • [Python Reference]
• Deep learning improves prediction of drug–drug and drug–food interactions (PNAS 2018)
  • Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee
  • [Paper]
  • [Python Reference]
• Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility (Toxicological Sciences 2018)
  • Thomas Luechtefeld, Dan Marsh, Craig Rowlands, Thomas Hartung
  • [Paper]
• A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (nature communications 2017)
  • Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen and Jianyang Zeng
  • [Paper]
  • [Python Reference]
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (Arxiv 2017)
  • Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu
  • [Paper]
  • [Python Reference]
• drugGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico (ACS 2017)
  • Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov
  • [Paper]
• Learning Graph-Level Representation for Drug Discovery (Arxiv 2017)
  • Junying Li, Deng Cai, Xiaofei He
  • [Paper]
  • [Python Reference]
• Deep-Learning-Based Drug–Target Interaction Prediction (ACS 2017)
  • Ming Wen, Zhimin Zhang, Shaoyu Niu, Haozhi Sha, Ruihan Yang, Yonghuan Yun, Hongmei Lu
  • [Paper]
  • [Python Reference]
• Machine learning accelerates MD-based binding (Bioinformatics 2017)
  • Kei Terayama, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, Koji Tsuda
  • [Paper]
  • [Python Reference]
• Deep learning with feature embedding for compound-protein interaction prediction (bioRxiv 2016)
  • Fangping Wan, Jianyang (Michael) Zeng
  • [Paper]
  • [Python Reference]
• CGBVS-DNN Prediction of Compound-protein Interactions Based on Deep Learning (2016)
  • Masatoshi Hamanaka, Kei Taneishi, Hiroaki Iwata, Jun Ye, Jianguo Pei, Jinlong Hou, Yasushi Okuno
  • [Paper]
• Boosting compound-protein interaction prediction by deep learning (2016)
  • Kai Tian, Mingyu Shao, Yang Wang, Jihong Guan, Shuigeng Zhou
  • [Paper]
• Boosting Docking-based Virtual Screening with Deep Learning (ACS 2016)
  • Janaina Cruz Pereira, Ernesto Raúl Caffarena, Cicero Nogueira dos Santos
  • [Paper]
• Massively Multitask Networks for Drug Discovery (CoRR 2015)
  • Bharath Ramsundar, Steven M. Kearnes, Patrick Riley, Dale Webster, David E. Konerding, Vijay S. Pande
  • [Paper]
• Deep Neural Nets as a Method for Quantitative Structure−Activity Relationships (ACS 2015)
  • Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik
  • [Paper]
• Toxicity Prediction using Deep Learning (Arxiv 2015)
  • Thomas Unterthiner
  • [Paper]
• Multi-Task Deep Networks for Drug Target Prediction (NIPS 2014)
  • Thomas Unterthiner, AndreasMayr, G¨unterKlambauer
  • [Paper]
• Multi-task Neural Networks for QSAR Predictions (Arxiv 2014)
  • George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
  • [Paper]
• Deep Learning as an Opportunity in Virtual Screening (2014)
  • Thomas Unterthiner
  • [Paper]

Recommender Systems

• Multi-Component Graph Convolutional Collaborative Filtering (AAAI 2020)
  • Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li
  • [Paper]
  • [Python Reference]
• SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation (ECML 2019)
  • Vijaikumar M, Shirish Shevade, and M N Murt
  • [Paper]
  • [Python Reference]
• AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (CIKM 2019)
  • Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang
  • [Paper]
  • [Python Reference1]
  • [Python Reference2]
• Neural Graph Collaborative Filtering (SIGIR 2019)
  • Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng and Tat-Seng Chua
  • [Paper]
  • [Python Reference]
• Collaborative Similarity Embedding for Recommender Systems (WWW 2019)
  • Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang
  • [Paper]
• Variational Autoencoders for Collaborative Filtering (WWW 2018)
  • Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
  • [Paper]
• TEM: Tree-enhancedEmbeddingModelfor ExplainableRecommendation (WWW 2018)
  • Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua
  • [Paper]
  • [Python Reference]
• Neural Collaborative Filtering (WWW 2017)
  • Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
  • [Paper]
  • [Python Reference(Keras)]
  • [Python Reference(Pytorch)]

Others

• A Degeneracy Framework for Graph Similarity (IJCAI 2018)
  • Giannis Nikolentzos, Polykarpos Meladianos, Stratis Limnios and Michalis Vazirgiannis
  • [Paper]
  • [Python Reference]
• Fast Graph Representation Learning with Pytorch Geometric (ICLR 2019)
  • Matthias Fey, Jan E. Lenssen
  • [Paper]
  • [Python Reference]
• GMNN: Graph Markov Neural Networks (ICML 2019)
  • Meng Qu, Yoshua Bengio, Jian Tang
  • [Paper]
  • [Slides]
  • [Python Reference]
• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (RecSys 2019)
  • Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
  • [Paper]
  • [Python Reference]