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https://github.com/xnuohz/awesome-drug-discovery
A collection of drug discovery, classification and representation learning papers with deep learning.
https://github.com/xnuohz/awesome-drug-discovery
List: awesome-drug-discovery
deep-learning drug-discovery recommender-system
Last synced: about 2 months ago
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A collection of drug discovery, classification and representation learning papers with deep learning.
- Host: GitHub
- URL: https://github.com/xnuohz/awesome-drug-discovery
- Owner: xnuohz
- License: mit
- Created: 2019-05-23T10:28:40.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-11-15T18:30:05.000Z (over 2 years ago)
- Last Synced: 2024-04-18T20:59:26.613Z (about 2 months ago)
- Topics: deep-learning, drug-discovery, recommender-system
- Size: 36.1 KB
- Stars: 32
- Watchers: 4
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-small-molecule-ml - Awesome Drug Discovery
- ultimate-awesome - awesome-drug-discovery - A collection of drug discovery, classification and representation learning papers with deep learning. (Other Lists / Julia Lists)
README
# awesome-drug-discovery
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
A collection of drug discovery, classification and representation learning papers with deep learning.
## Tutorial
- [torch_geometric for chemoinformatics](https://iwatobipen.wordpress.com/2019/04/05/make-graph-convolution-model-with-geometric-deep-learning-extension-library-for-pytorch-rdkit-chemoinformatics-pytorch/)
## 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)]](https://www.nature.com/articles/s41573-019-0024-5)
- [[Paper(sci-hub)]](https://sci-hub.tw/10.1038/s41573-019-0024-5)
- **Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics (bioRxiv 2019)**
- Benoit Playe, Véronique Stoven
- [[Paper]](https://www.biorxiv.org/content/10.1101/662098v1)
- [[Python Reference]](https://github.com/bplaye/NNk_DTI)
- **Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (Chemical Science 2019)**
- Andreas Mayr et.
- [[Paper]](https://pubs.rsc.org/en/content/articlelanding/2018/sc/c8sc00148k#!divAbstract)
- **PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction (Arxiv 2018)**
- Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, Martin Ester
- [[Paper]](https://arxiv.org/abs/1807.09741)
- [[Python Reference]](https://github.com/simonfqy/PADME)
## 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]](https://www.nature.com/articles/s41467-019-12928-6)
- **Drug repositioning based on bounded nuclear norm regularization (ISMB/ECCB 2019)**
- Mengyun Yang, Huimin Luo, Yaohang Li and Jianxin Wang
- [[Paper]](https://academic.oup.com/bioinformatics/article/35/14/i455/5529141)
- [[Matlab Reference]](https://github.com/BioinformaticsCSU/BNNR)
## 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]](https://www.biorxiv.org/content/10.1101/2019.12.30.891515v1)
- [[Python Reference]](https://github.com/lishuya17/MONN)
- **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]](https://arxiv.org/abs/1906.08230)
- [[Python Reference(pytorch)]](https://github.com/songlab-cal/tape)
- [[Python Reference(tensorflow)]](https://github.com/songlab-cal/tape-neurips2019)
- **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]](https://pubs.acs.org/doi/10.1021/acs.jcim.9b00387)
- [[Python Reference]](https://github.com/jaechanglim/GNN_DTI)
- **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]](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00672)
- [[Python Reference]](https://github.com/MingCPU/DeepChemStable)
- **DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (PLOS 2019)**
- Ingoo LeeID, Jongsoo Keum, Hojung NamID
- [[Paper]](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007129)
- [[Python Reference]](https://github.com/GIST-CSBL/DeepConv-DTI)
- **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]](https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00959)
- [[Python Reference]](https://github.com/OpenDrugAI/AttentiveFP)
- **GraphDTA: prediction of drug–target binding affinity using graph convolutional networks (BioArxiv 2019)**
- Thin Nguyen, Hang Le, Svetha Venkatesh
- [[Paper]](https://www.biorxiv.org/content/10.1101/684662v3)
- [[Python Reference]](https://github.com/thinng/GraphDTA)
- **Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction (2019)**
- Bonggun Shin
- [[Paper]](https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/5d472f63eebdc3000174efea/1564946292553/Shin.pdf)
- **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]](https://academic.oup.com/bioinformatics/article/35/14/i305/5529260)
- [[Python Reference]](https://github.com/muhaochen/seq_ppi)
- **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]](https://arxiv.org/abs/1906.05168)
- [[Python Reference]](https://github.com/lehgtrung/egfr-att)
- **LEARNING PROTEIN SEQUENCE EMBEDDINGS USING INFORMATION FROM STRUCTURE (ICLR 2019)**
- Tristan Bepler, Bonnie Berger
- [[Paper]](https://openreview.net/pdf?id=SygLehCqtm)
- [[Python Reference]](https://github.com/tbepler/protein-sequence-embedding-iclr2019)
- **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]](https://academic.oup.com/bioinformatics/article/35/1/104/5047760)
- [[Python Reference]](https://github.com/FangpingWan/NeoDTI)
- **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]](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz111/5320555)
- [[Python Reference]](https://github.com/Shen-Lab/DeepAffinity)
- **WideDTA: prediction of drug-target binding affinity (Arxiv 2019)**
- Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
- [[Paper]](https://arxiv.org/abs/1902.04166)
- [[Python Reference]](https://github.com/hkmztrk/WideDTA)
- **Predicting Drug Protein Interaction using Quasi-Visual Question Answering System (bioRxiv 2019)**
- Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang
- [[Paper]](https://www.biorxiv.org/content/10.1101/588178v1)
- **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]](https://ieeexplore.ieee.org/abstract/document/8621390)
- **Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (ACS 2018)**
- Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud
- [[Paper]](https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00572)
- [[Python Reference]](https://github.com/aspuru-guzik-group/chemical_vae)
- **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]](https://academic.oup.com/bioinformatics/article/35/2/309/5050020)
- [[Python Reference]](https://github.com/masashitsubaki/CPI_prediction)
- **Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (KDD 2018)**
- Shahar Harel, Kira Radinsky
- [[Paper]](http://www.kiraradinsky.com/files/accelerating-prototype-based.pdf)
- [[Python Reference]](https://github.com/shaharharel/CDN_Molecule)
- **DeepDTA: deep drug–target binding affinity prediction (Bioinformatics 2018)**
- Hakime Öztürk, Arzucan Özgür, Elif Ozkirimli
- [[Paper]](https://academic.oup.com/bioinformatics/article/34/17/i821/5093245)
- [[Python Reference]](https://github.com/hkmztrk/DeepDTA)
- **Interpretable Drug Target Prediction Using Deep Neural Representation (IJCAI 2018)**
- Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, Ping Zhang
- [[Paper]](https://pdfs.semanticscholar.org/693c/c33b99ca3781062e77e2e5cd45191632e683.pdf)
- **Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (bioRxiv 2018)**
- Wen Torng, Russ B. Altman
- [[Paper]](https://www.biorxiv.org/content/10.1101/473074v1)
- **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]](https://arxiv.org/abs/1803.06236)
- **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]](https://arxiv.org/abs/1811.08283)
- [[Python Reference]](https://github.com/paularindam/CheMixNet)
- **Deep learning improves prediction of drug–drug and drug–food interactions (PNAS 2018)**
- Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee
- [[Paper]](https://www.pnas.org/content/115/18/E4304.short)
- [[Python Reference]](https://bitbucket.org/kaistsystemsbiology/deepddi/src/master/)
- **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]](https://academic.oup.com/toxsci/article/165/1/198/5043469)
- **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]](https://www.nature.com/articles/s41467-017-00680-8)
- [[Python Reference]](https://github.com/luoyunan/DTINet)
- **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]](https://arxiv.org/abs/1712.02034)
- [[Python Reference]](https://github.com/Abdulk084/Smiles2vec)
- **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]](https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.7b00346)
- **Learning Graph-Level Representation for Drug Discovery (Arxiv 2017)**
- Junying Li, Deng Cai, Xiaofei He
- [[Paper]](https://arxiv.org/abs/1709.03741)
- [[Python Reference]](https://github.com/ZJULearning/graph_level_drug_discovery)
- **Deep-Learning-Based Drug–Target Interaction Prediction (ACS 2017)**
- Ming Wen, Zhimin Zhang, Shaoyu Niu, Haozhi Sha, Ruihan Yang, Yonghuan Yun, Hongmei Lu
- [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00618)
- [[Python Reference]](https://github.com/Bjoux2/DeepDTIs_DBN)
- **Machine learning accelerates MD-based binding (Bioinformatics 2017)**
- Kei Terayama, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, Koji Tsuda
- [[Paper]](https://academic.oup.com/bioinformatics/article/34/5/770/4457357)
- [[Python Reference]](https://github.com/tsudalab/bpbi)
- **Deep learning with feature embedding for compound-protein interaction prediction (bioRxiv 2016)**
- Fangping Wan, Jianyang (Michael) Zeng
- [[Paper]](https://www.biorxiv.org/content/10.1101/086033v1)
- [[Python Reference]](https://github.com/FangpingWan/DeepCPI)
- **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]](https://onlinelibrary.wiley.com/doi/10.1002/minf.201600045)
- **Boosting compound-protein interaction prediction by deep learning (2016)**
- Kai Tian, Mingyu Shao, Yang Wang, Jihong Guan, Shuigeng Zhou
- [[Paper]](https://www.sciencedirect.com/science/article/pii/S1046202316301992)
- **Boosting Docking-based Virtual Screening with Deep Learning (ACS 2016)**
- Janaina Cruz Pereira, Ernesto Raúl Caffarena, Cicero Nogueira dos Santos
- [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00355)
- **Massively Multitask Networks for Drug Discovery (CoRR 2015)**
- Bharath Ramsundar, Steven M. Kearnes, Patrick Riley, Dale Webster, David E. Konerding, Vijay S. Pande
- [[Paper]](https://arxiv.org/abs/1502.02072)
- **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]](https://pubs.acs.org/doi/abs/10.1021/ci500747n)
- **Toxicity Prediction using Deep Learning (Arxiv 2015)**
- Thomas Unterthiner
- [[Paper]](https://arxiv.org/abs/1503.01445)
- **Multi-Task Deep Networks for Drug Target Prediction (NIPS 2014)**
- Thomas Unterthiner, AndreasMayr, G¨unterKlambauer
- [[Paper]](http://www.bioinf.at/publications/2014/NIPS2014d.pdf)
- **Multi-task Neural Networks for QSAR Predictions (Arxiv 2014)**
- George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
- [[Paper]](https://arxiv.org/abs/1406.1231)
- **Deep Learning as an Opportunity in Virtual Screening (2014)**
- Thomas Unterthiner
- [[Paper]](https://pdfs.semanticscholar.org/95f7/b2c0fe75f08e3ce0d2ac4315166f4239db5c.pdf)
## Recommender Systems
- **Multi-Component Graph Convolutional Collaborative Filtering (AAAI 2020)**
- Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li
- [[Paper]](https://arxiv.org/abs/1911.10699)
- [[Python Reference]](https://github.com/RuijiaW/Multi-Component-Graph-Convolutional-Collaborative-Filtering)
- **SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation (ECML 2019)**
- Vijaikumar M, Shirish Shevade, and M N Murt
- [[Paper]](https://ecmlpkdd2019.org/downloads/paper/475.pdf)
- [[Python Reference]](https://github.com/mvijaikumar/SoRecGAT)
- **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]](https://arxiv.org/abs/1810.11921)
- [[Python Reference1]](https://github.com/DeepGraphLearning/RecommenderSystems)
- [[Python Reference2]](https://github.com/shenweichen/DeepCTR-Torch)
- **Neural Graph Collaborative Filtering (SIGIR 2019)**
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng and Tat-Seng Chua
- [[Paper]](https://arxiv.org/abs/1905.08108)
- [[Python Reference]](https://github.com/xiangwang1223/neural_graph_collaborative_filtering)
- **Collaborative Similarity Embedding for Recommender Systems (WWW 2019)**
- Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang
- [[Paper]](https://arxiv.org/abs/1902.06188)
- **Variational Autoencoders for Collaborative Filtering (WWW 2018)**
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
- [[Paper]](https://arxiv.org/abs/1802.05814)
- **TEM: Tree-enhancedEmbeddingModelfor ExplainableRecommendation (WWW 2018)**
- Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua
- [[Paper]](http://staff.ustc.edu.cn/~hexn/papers/www18-tem.pdf)
- [[Python Reference]](https://github.com/xiangwang1223/tree_enhanced_embedding_model)
- **Neural Collaborative Filtering (WWW 2017)**
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
- [[Paper]](https://arxiv.org/abs/1708.05031)
- [[Python Reference(Keras)]](https://github.com/hexiangnan/neural_collaborative_filtering)
- [[Python Reference(Pytorch)]](https://github.com/LaceyChen17/neural-collaborative-filtering)
## Others
- **A Degeneracy Framework for Graph Similarity (IJCAI 2018)**
- Giannis Nikolentzos, Polykarpos Meladianos, Stratis Limnios and Michalis Vazirgiannis
- [[Paper]](https://www.ijcai.org/proceedings/2018/360)
- [[Python Reference]](https://github.com/xnuohz/graph-kernel)
- **Fast Graph Representation Learning with Pytorch Geometric (ICLR 2019)**
- Matthias Fey, Jan E. Lenssen
- [[Paper]](https://rlgm.github.io/papers/2.pdf)
- [[Python Reference]](https://rusty1s.github.io/pytorch_geometric)
- **GMNN: Graph Markov Neural Networks (ICML 2019)**
- Meng Qu, Yoshua Bengio, Jian Tang
- [[Paper]](http://proceedings.mlr.press/v97/qu19a/qu19a.pdf)
- [[Slides]](https://icml.cc/media/Slides/icml/2019/halla(11-11-00)-11-12-00-4516-gmnn_graph_mar.pdf)
- [[Python Reference]](https://github.com/DeepGraphLearning/GMNN)
- **Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (RecSys 2019)**
- Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
- [[Paper]](https://arxiv.org/pdf/1907.06902v2.pdf)
- [[Python Reference]](https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation)