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awesome-drug-discovery

Awesome PRs Welcome

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

Tutorial

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)
  • Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (Chemical Science 2019)
  • PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction (Arxiv 2018)

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)

Deep Learning

  • MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins (RECOMB 2020)
  • Evaluating Protein Transfer Learning with TAPE (NIPS 2019)
  • Predicting Drug−Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (ACS 2019)
  • DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network (ACS 2019)
  • DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (PLOS 2019)
  • 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)
  • Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction (2019)
  • 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)
  • LEARNING PROTEIN SEQUENCE EMBEDDINGS USING INFORMATION FROM STRUCTURE (ICLR 2019)
  • NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions (Bioinformatics 2019)
  • DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks (Bioinformatics 2019)
  • WideDTA: prediction of drug-target binding affinity (Arxiv 2019)
  • 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)
  • Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences (Bioinformatics 2018)
  • Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (KDD 2018)
  • DeepDTA: deep drug–target binding affinity prediction (Bioinformatics 2018)
  • 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)
  • Deep learning improves prediction of drug–drug and drug–food interactions (PNAS 2018)
  • 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)
  • 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)
  • Deep-Learning-Based Drug–Target Interaction Prediction (ACS 2017)
  • Machine learning accelerates MD-based binding (Bioinformatics 2017)
  • Deep learning with feature embedding for compound-protein interaction prediction (bioRxiv 2016)
  • 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)
  • 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)

Recommender Systems

  • Multi-Component Graph Convolutional Collaborative Filtering (AAAI 2020)
  • SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation (ECML 2019)
  • AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (CIKM 2019)
  • Neural Graph Collaborative Filtering (SIGIR 2019)
  • 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)
  • Neural Collaborative Filtering (WWW 2017)

Others

  • A Degeneracy Framework for Graph Similarity (IJCAI 2018)
  • Fast Graph Representation Learning with Pytorch Geometric (ICLR 2019)
  • GMNN: Graph Markov Neural Networks (ICML 2019)
  • Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (RecSys 2019)