├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 xnouhz 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awesome-drug-discovery 2 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 3 | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) 4 | 5 | A collection of drug discovery, classification and representation learning papers with deep learning. 6 | 7 | ## Tutorial 8 | 9 | - [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/) 10 | 11 | ## Survey 12 | 13 | - **Applications of machine learning in drug discovery and development (Nature Reviews drug discovery 2019)** 14 | - Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer & Shanrong Zhao 15 | - [[Paper(nature)]](https://www.nature.com/articles/s41573-019-0024-5) 16 | - [[Paper(sci-hub)]](https://sci-hub.tw/10.1038/s41573-019-0024-5) 17 | - **Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics (bioRxiv 2019)** 18 | - Benoit Playe, Véronique Stoven 19 | - [[Paper]](https://www.biorxiv.org/content/10.1101/662098v1) 20 | - [[Python Reference]](https://github.com/bplaye/NNk_DTI) 21 | - **Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (Chemical Science 2019)** 22 | - Andreas Mayr et. 23 | - [[Paper]](https://pubs.rsc.org/en/content/articlelanding/2018/sc/c8sc00148k#!divAbstract) 24 | - **PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction (Arxiv 2018)** 25 | - Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, Martin Ester 26 | - [[Paper]](https://arxiv.org/abs/1807.09741) 27 | - [[Python Reference]](https://github.com/simonfqy/PADME) 28 | 29 | 30 | ## Tradintional Machine Learning 31 | 32 | - **A Bayesian machine learning approach for drug target identification using diverse data types (Nature Communications 2019)** 33 | - Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Martin Stogniew, Joshua E. Allen, Paraskevi Giannakakou & Olivier Elemento 34 | - [[Paper]](https://www.nature.com/articles/s41467-019-12928-6) 35 | - **Drug repositioning based on bounded nuclear norm regularization (ISMB/ECCB 2019)** 36 | - Mengyun Yang, Huimin Luo, Yaohang Li and Jianxin Wang 37 | - [[Paper]](https://academic.oup.com/bioinformatics/article/35/14/i455/5529141) 38 | - [[Matlab Reference]](https://github.com/BioinformaticsCSU/BNNR) 39 | 40 | ## Deep Learning 41 | 42 | - **MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins (RECOMB 2020)** 43 | - Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, Jianyang Zeng 44 | - [[Paper]](https://www.biorxiv.org/content/10.1101/2019.12.30.891515v1) 45 | - [[Python Reference]](https://github.com/lishuya17/MONN) 46 | - **Evaluating Protein Transfer Learning with TAPE (NIPS 2019)** 47 | - Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song 48 | - [[Paper]](https://arxiv.org/abs/1906.08230) 49 | - [[Python Reference(pytorch)]](https://github.com/songlab-cal/tape) 50 | - [[Python Reference(tensorflow)]](https://github.com/songlab-cal/tape-neurips2019) 51 | - **Predicting Drug−Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (ACS 2019)** 52 | - Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham and Woo Youn Kim 53 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jcim.9b00387) 54 | - [[Python Reference]](https://github.com/jaechanglim/GNN_DTI) 55 | - **DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network (ACS 2019)** 56 | - Xiuming Li, Xin Yan, Qiong Gu, Huihao Zhou, Di Wu and Jun Xu 57 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00672) 58 | - [[Python Reference]](https://github.com/MingCPU/DeepChemStable) 59 | - **DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (PLOS 2019)** 60 | - Ingoo LeeID, Jongsoo Keum, Hojung NamID 61 | - [[Paper]](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007129) 62 | - [[Python Reference]](https://github.com/GIST-CSBL/DeepConv-DTI) 63 | - **A Domain Knowledge Constraint Variantional Model for Drug Discovery (AAAI 2020 preprint review)** 64 | - **DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction (AAAI 2020 preprint review)** 65 | - **DAEM: Deep Attribute Embedding based Multi-Task Learning for Predicting Adverse Drug-Drug Interaction (AAAI 2020 preprint review)** 66 | - **Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism (Journal of Medicinal Chemistry 2019)** 67 | - Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang and Mingyue Zheng 68 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00959) 69 | - [[Python Reference]](https://github.com/OpenDrugAI/AttentiveFP) 70 | - **GraphDTA: prediction of drug–target binding affinity using graph convolutional networks (BioArxiv 2019)** 71 | - Thin Nguyen, Hang Le, Svetha Venkatesh 72 | - [[Paper]](https://www.biorxiv.org/content/10.1101/684662v3) 73 | - [[Python Reference]](https://github.com/thinng/GraphDTA) 74 | - **Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction (2019)** 75 | - Bonggun Shin 76 | - [[Paper]](https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/5d472f63eebdc3000174efea/1564946292553/Shin.pdf) 77 | - **Multifaceted protein–protein interaction prediction based on Siamese residual RCNN (ISMB/ECCB 2019)** 78 | - Muhao Chen1, Chelsea J.-T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo and Wei Wang 79 | - [[Paper]](https://academic.oup.com/bioinformatics/article/35/14/i305/5529260) 80 | - [[Python Reference]](https://github.com/muhaochen/seq_ppi) 81 | - **Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors (Arxiv 2019)** 82 | - Huy Ngoc Pham, Trung Hoang Le 83 | - [[Paper]](https://arxiv.org/abs/1906.05168) 84 | - [[Python Reference]](https://github.com/lehgtrung/egfr-att) 85 | - **LEARNING PROTEIN SEQUENCE EMBEDDINGS USING INFORMATION FROM STRUCTURE (ICLR 2019)** 86 | - Tristan Bepler, Bonnie Berger 87 | - [[Paper]](https://openreview.net/pdf?id=SygLehCqtm) 88 | - [[Python Reference]](https://github.com/tbepler/protein-sequence-embedding-iclr2019) 89 | - **NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions (Bioinformatics 2019)** 90 | - Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng 91 | - [[Paper]](https://academic.oup.com/bioinformatics/article/35/1/104/5047760) 92 | - [[Python Reference]](https://github.com/FangpingWan/NeoDTI) 93 | - **DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks (Bioinformatics 2019)** 94 | - Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen 95 | - [[Paper]](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz111/5320555) 96 | - [[Python Reference]](https://github.com/Shen-Lab/DeepAffinity) 97 | - **WideDTA: prediction of drug-target binding affinity (Arxiv 2019)** 98 | - Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür 99 | - [[Paper]](https://arxiv.org/abs/1902.04166) 100 | - [[Python Reference]](https://github.com/hkmztrk/WideDTA) 101 | - **Predicting Drug Protein Interaction using Quasi-Visual Question Answering System (bioRxiv 2019)** 102 | - Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang 103 | - [[Paper]](https://www.biorxiv.org/content/10.1101/588178v1) 104 | - **Drug2Vec: Knowledge-aware Feature-driven Method for Drug Representation Learning (BIBM 2018)** 105 | - Ying Shen, Kaiqi Yuan, Yaliang Li, Buzhou Tang, Min Yang, Nan Du, Kai Lei 106 | - [[Paper]](https://ieeexplore.ieee.org/abstract/document/8621390) 107 | - **Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (ACS 2018)** 108 | - Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud 109 | - [[Paper]](https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00572) 110 | - [[Python Reference]](https://github.com/aspuru-guzik-group/chemical_vae) 111 | - **Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences (Bioinformatics 2018)** 112 | - Masashi Tsubaki, Kentaro Tomii, Jun Sese 113 | - [[Paper]](https://academic.oup.com/bioinformatics/article/35/2/309/5050020) 114 | - [[Python Reference]](https://github.com/masashitsubaki/CPI_prediction) 115 | - **Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (KDD 2018)** 116 | - Shahar Harel, Kira Radinsky 117 | - [[Paper]](http://www.kiraradinsky.com/files/accelerating-prototype-based.pdf) 118 | - [[Python Reference]](https://github.com/shaharharel/CDN_Molecule) 119 | - **DeepDTA: deep drug–target binding affinity prediction (Bioinformatics 2018)** 120 | - Hakime Öztürk, Arzucan Özgür, Elif Ozkirimli 121 | - [[Paper]](https://academic.oup.com/bioinformatics/article/34/17/i821/5093245) 122 | - [[Python Reference]](https://github.com/hkmztrk/DeepDTA) 123 | - **Interpretable Drug Target Prediction Using Deep Neural Representation (IJCAI 2018)** 124 | - Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, Ping Zhang 125 | - [[Paper]](https://pdfs.semanticscholar.org/693c/c33b99ca3781062e77e2e5cd45191632e683.pdf) 126 | - **Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (bioRxiv 2018)** 127 | - Wen Torng, Russ B. Altman 128 | - [[Paper]](https://www.biorxiv.org/content/10.1101/473074v1) 129 | - **Chemi-Net: A molecular graph convolutional network for accurate drug property prediction (Arxiv 2018)** 130 | - Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois, Jie Fan 131 | - [[Paper]](https://arxiv.org/abs/1803.06236) 132 | - **CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations (CoRR 2018)** 133 | - Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok N. Choudhary, Ankit Agrawal 134 | - [[Paper]](https://arxiv.org/abs/1811.08283) 135 | - [[Python Reference]](https://github.com/paularindam/CheMixNet) 136 | - **Deep learning improves prediction of drug–drug and drug–food interactions (PNAS 2018)** 137 | - Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee 138 | - [[Paper]](https://www.pnas.org/content/115/18/E4304.short) 139 | - [[Python Reference]](https://bitbucket.org/kaistsystemsbiology/deepddi/src/master/) 140 | - **Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility (Toxicological Sciences 2018)** 141 | - Thomas Luechtefeld, Dan Marsh, Craig Rowlands, Thomas Hartung 142 | - [[Paper]](https://academic.oup.com/toxsci/article/165/1/198/5043469) 143 | - **A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (nature communications 2017)** 144 | - Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen and Jianyang Zeng 145 | - [[Paper]](https://www.nature.com/articles/s41467-017-00680-8) 146 | - [[Python Reference]](https://github.com/luoyunan/DTINet) 147 | - **SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (Arxiv 2017)** 148 | - Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu 149 | - [[Paper]](https://arxiv.org/abs/1712.02034) 150 | - [[Python Reference]](https://github.com/Abdulk084/Smiles2vec) 151 | - **drugGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico (ACS 2017)** 152 | - Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov 153 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.7b00346) 154 | - **Learning Graph-Level Representation for Drug Discovery (Arxiv 2017)** 155 | - Junying Li, Deng Cai, Xiaofei He 156 | - [[Paper]](https://arxiv.org/abs/1709.03741) 157 | - [[Python Reference]](https://github.com/ZJULearning/graph_level_drug_discovery) 158 | - **Deep-Learning-Based Drug–Target Interaction Prediction (ACS 2017)** 159 | - Ming Wen, Zhimin Zhang, Shaoyu Niu, Haozhi Sha, Ruihan Yang, Yonghuan Yun, Hongmei Lu 160 | - [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00618) 161 | - [[Python Reference]](https://github.com/Bjoux2/DeepDTIs_DBN) 162 | - **Machine learning accelerates MD-based binding (Bioinformatics 2017)** 163 | - Kei Terayama, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, Koji Tsuda 164 | - [[Paper]](https://academic.oup.com/bioinformatics/article/34/5/770/4457357) 165 | - [[Python Reference]](https://github.com/tsudalab/bpbi) 166 | - **Deep learning with feature embedding for compound-protein interaction prediction (bioRxiv 2016)** 167 | - Fangping Wan, Jianyang (Michael) Zeng 168 | - [[Paper]](https://www.biorxiv.org/content/10.1101/086033v1) 169 | - [[Python Reference]](https://github.com/FangpingWan/DeepCPI) 170 | - **CGBVS-DNN Prediction of Compound-protein Interactions Based on Deep Learning (2016)** 171 | - Masatoshi Hamanaka, Kei Taneishi, Hiroaki Iwata, Jun Ye, Jianguo Pei, Jinlong Hou, Yasushi Okuno 172 | - [[Paper]](https://onlinelibrary.wiley.com/doi/10.1002/minf.201600045) 173 | - **Boosting compound-protein interaction prediction by deep learning (2016)** 174 | - Kai Tian, Mingyu Shao, Yang Wang, Jihong Guan, Shuigeng Zhou 175 | - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1046202316301992) 176 | - **Boosting Docking-based Virtual Screening with Deep Learning (ACS 2016)** 177 | - Janaina Cruz Pereira, Ernesto Raúl Caffarena, Cicero Nogueira dos Santos 178 | - [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00355) 179 | - **Massively Multitask Networks for Drug Discovery (CoRR 2015)** 180 | - Bharath Ramsundar, Steven M. Kearnes, Patrick Riley, Dale Webster, David E. Konerding, Vijay S. Pande 181 | - [[Paper]](https://arxiv.org/abs/1502.02072) 182 | - **Deep Neural Nets as a Method for Quantitative Structure−Activity Relationships (ACS 2015)** 183 | - Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik 184 | - [[Paper]](https://pubs.acs.org/doi/abs/10.1021/ci500747n) 185 | - **Toxicity Prediction using Deep Learning (Arxiv 2015)** 186 | - Thomas Unterthiner 187 | - [[Paper]](https://arxiv.org/abs/1503.01445) 188 | - **Multi-Task Deep Networks for Drug Target Prediction (NIPS 2014)** 189 | - Thomas Unterthiner, AndreasMayr, G¨unterKlambauer 190 | - [[Paper]](http://www.bioinf.at/publications/2014/NIPS2014d.pdf) 191 | - **Multi-task Neural Networks for QSAR Predictions (Arxiv 2014)** 192 | - George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov 193 | - [[Paper]](https://arxiv.org/abs/1406.1231) 194 | - **Deep Learning as an Opportunity in Virtual Screening (2014)** 195 | - Thomas Unterthiner 196 | - [[Paper]](https://pdfs.semanticscholar.org/95f7/b2c0fe75f08e3ce0d2ac4315166f4239db5c.pdf) 197 | 198 | ## Recommender Systems 199 | 200 | - **Multi-Component Graph Convolutional Collaborative Filtering (AAAI 2020)** 201 | - Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li 202 | - [[Paper]](https://arxiv.org/abs/1911.10699) 203 | - [[Python Reference]](https://github.com/RuijiaW/Multi-Component-Graph-Convolutional-Collaborative-Filtering) 204 | - **SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation (ECML 2019)** 205 | - Vijaikumar M, Shirish Shevade, and M N Murt 206 | - [[Paper]](https://ecmlpkdd2019.org/downloads/paper/475.pdf) 207 | - [[Python Reference]](https://github.com/mvijaikumar/SoRecGAT) 208 | - **AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (CIKM 2019)** 209 | - Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang 210 | - [[Paper]](https://arxiv.org/abs/1810.11921) 211 | - [[Python Reference1]](https://github.com/DeepGraphLearning/RecommenderSystems) 212 | - [[Python Reference2]](https://github.com/shenweichen/DeepCTR-Torch) 213 | - **Neural Graph Collaborative Filtering (SIGIR 2019)** 214 | - Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng and Tat-Seng Chua 215 | - [[Paper]](https://arxiv.org/abs/1905.08108) 216 | - [[Python Reference]](https://github.com/xiangwang1223/neural_graph_collaborative_filtering) 217 | - **Collaborative Similarity Embedding for Recommender Systems (WWW 2019)** 218 | - Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang 219 | - [[Paper]](https://arxiv.org/abs/1902.06188) 220 | - **Variational Autoencoders for Collaborative Filtering (WWW 2018)** 221 | - Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara 222 | - [[Paper]](https://arxiv.org/abs/1802.05814) 223 | - **TEM: Tree-enhancedEmbeddingModelfor ExplainableRecommendation (WWW 2018)** 224 | - Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua 225 | - [[Paper]](http://staff.ustc.edu.cn/~hexn/papers/www18-tem.pdf) 226 | - [[Python Reference]](https://github.com/xiangwang1223/tree_enhanced_embedding_model) 227 | - **Neural Collaborative Filtering (WWW 2017)** 228 | - Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua 229 | - [[Paper]](https://arxiv.org/abs/1708.05031) 230 | - [[Python Reference(Keras)]](https://github.com/hexiangnan/neural_collaborative_filtering) 231 | - [[Python Reference(Pytorch)]](https://github.com/LaceyChen17/neural-collaborative-filtering) 232 | 233 | ## Others 234 | 235 | - **A Degeneracy Framework for Graph Similarity (IJCAI 2018)** 236 | - Giannis Nikolentzos, Polykarpos Meladianos, Stratis Limnios and Michalis Vazirgiannis 237 | - [[Paper]](https://www.ijcai.org/proceedings/2018/360) 238 | - [[Python Reference]](https://github.com/xnuohz/graph-kernel) 239 | - **Fast Graph Representation Learning with Pytorch Geometric (ICLR 2019)** 240 | - Matthias Fey, Jan E. Lenssen 241 | - [[Paper]](https://rlgm.github.io/papers/2.pdf) 242 | - [[Python Reference]](https://rusty1s.github.io/pytorch_geometric) 243 | - **GMNN: Graph Markov Neural Networks (ICML 2019)** 244 | - Meng Qu, Yoshua Bengio, Jian Tang 245 | - [[Paper]](http://proceedings.mlr.press/v97/qu19a/qu19a.pdf) 246 | - [[Slides]](https://icml.cc/media/Slides/icml/2019/halla(11-11-00)-11-12-00-4516-gmnn_graph_mar.pdf) 247 | - [[Python Reference]](https://github.com/DeepGraphLearning/GMNN) 248 | - **Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (RecSys 2019)** 249 | - Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach 250 | - [[Paper]](https://arxiv.org/pdf/1907.06902v2.pdf) 251 | - [[Python Reference]](https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation) 252 | --------------------------------------------------------------------------------