This is a paper list of deep learning on graphs in chemistry and biology from ML community, chemistry community and biology community.
This is inspired by the Literature of Deep Learning for Graphs project.
- The Rise of Deep Learning in Drug Discovery
- Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke
Drug Discov Today, 2018, 23, 6
property and activity prediction, de novo design, reaction prediction, retrosynthetic analysis, ligand–protein interactions, biological imaging analysis - Opportunities and obstacles for deep learning in biology and medicine
- Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, Christopher A. Lavender, Srinivas C. Turaga, Amr M. Alexandari, Zhiyong Lu, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H. S. Segler, Simina M. Boca, S. Joshua Swamidass, Austin Huang, Anthony Gitter and Casey S. Greene
Journal of the Royal Society Interface, 2018, Volume 15, Issue 141
Protein-protein interaction networks and graph analysis, Chemical featurization and representation learning - Applications of Machine Learning in Drug Discovery and Development
- Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, Shanrong Zhao
Nature Reviews Drug Discovery 18
target identification, molecule optimization, biomarker discovery, computational pathology - Deep learning for molecular design—a review of the state of the art
- Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, Peter W. Chunga
Molecular Systems Design & Engineering, 2019, 4
molecular representation, deep learning architectures, evaluation, prospective and future directions - Graph convolutional networks for computational drug development and discovery
- Mengying Sun, Sendong Zhao, Coryandar Gilvary, Olivier Elemento, Jiayu Zhou, Fei Wang
Briefings in Bioinformatics, bbz042
graph neural networks, QSAR, biological property and activity, quantum mechanical property, interaction prediction, ligand–protein (drug–target) interaction, protein-protein interaction, drug-drug interaction, synthesis prediction, de novo molecular design - Generative Models for Automatic Chemical Design
- Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli
arXiv 1907
inverse design, generative models, prospects, challenges
- MoleculeNet: A Benchmark for Molecular Machine Learning
- Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande
Journal of Chemical Sciences, 2018, 9
property prediction, public datasets, evaluation metrics, baseline results, quantum mechanics, physical chemistry, biophysics, physiology
Website - Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
- Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang
arXiv 1906
property prediction, public datasets, baseline results, quantum mechanics
Website
- GuacaMol: Benchmarking Models for De Novo Molecular Design
- Nathan Brown, Marco Fiscato, Marwin H.S. Segler, Alain C. Vaucher
Journal of Chemical Information and Modeling, 2019, 59, 3
ChEMBL, public datasets, evaluation metrics, baseline results, distribution learning, goal-directed optimization
Github - Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Sergey Nikolenko, Alan Aspuru-Guzik, Alex Zhavoronkov
arXiv 1811
ZINC, public datasets, evaluation metrics, baseline results, distribution-learning
Github
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
NeurIPS 2015
graph neural networks
Github - Molecular graph convolutions: moving beyond fingerprints
- Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley
Journal of Computer-Aided Molecular Design, 2016, 30, 8
graph neural networks - Low Data Drug Discovery with One-shot Learning
- Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, Vijay Pande
ACS Central Science, 2017, 3, 4
graph neural networks, one-shot learning - Quantum-chemical Insights from Deep Tensor Neural Networks
- Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, Alexandre Tkatchenko
Nature Communications 8
graph neural networks, quantum mechanics - Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
- Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg, Vijay S. Pande
arXiv 1703
graph neural networks, protein-ligand binding affinity, PDBBind, nearest neighbor graphs - Neural Message Passing for Quantum Chemistry
- Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
ICML 2017
graph neural networks, quantum mechanics
Github - Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
- Youjun Xu, Jianfeng Pei, Luhua Lai
Journal of Chemical Information and Modeling 2017, 57, 11
graph neural networks - Deriving Neural Architectures from Sequence and Graph Kernels
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
- Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
arXiv 1706
graph neural networks, quantum mechanics
Github - Learning Graph-Level Representation for Drug Discovery
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation 2017, 13, 11
graph neural networks, benchmark results - Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
- Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola
NeurIPS 2017
graph neural networks, reaction prediction
Github - Protein Interface Prediction Using Graph Convolutional Networks
- Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
NeurIPS 2017
graph neural networks, protein interface prediction
Github - Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
- Connor W. Coley, Regina Barzilay, William H. Green, Tommi S. Jaakkola, Klavs F. Jensen
Journal of Chemical Information and Modeling, 2017, 57, 8
graph neural networks
Github - Learning a Local-Variable Model of Aromatic and Conjugated Systems
- Matthew K. Matlock, Na Le Dang and S. Joshua Swamidass
ACS Central Science, 2018, 4, 1
graph neural networks, weave, wave, quantum chemistry, adversarial - PotentialNet for Molecular Property Prediction
- Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S. Pande
ACS Central Science 2018, 4, 11
graph neural networks, protein-ligand binding affinity, metric - Chemi-net: a graph convolutional network for accurate drug property prediction
- Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois, Jie Fan
arXiv 1803
graph neural networks - Deeply Learning Molecular Structure-property Relationships Using Attention and Gate-augmented Graph Convolutional Network
- Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
- Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt
arXiv 1806
graph neural networks - Modeling polypharmacy side effects with graph convolutional networks
- Marinka Zitnik, Monica Agrawal, Jure Leskovec
Bioinformatics, Volume 34, Issue 13, 01 July 2018
graph neural networks, polypharmacy side effects, interaction prediction, multi-relation
Github - BayesGrad: Explaining Predictions of Graph Convolutional Networks
- Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu, Yohei Sugawara, Shin-ichi Maeda, Yukino Baba, Hisashi Kashima
arXiv 1807
graph neural networks, interpretability - Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
- Wen Torng, Russ B. Altman
bioRXiv
graph neural networks, auto encoders, interaction prediction - Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
- Hyeoncheol Cho, Insung S. Choi
arXiv 1811
graph neural networks, property prediction, interpretability - A graph-convolutional neural network model for the prediction of chemical reactivity
- Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, Klavs F. Jensen
Chemical Science, 2019, 10
graph neural networks, reaction prediction
Github - NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
- Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng
Bioinformatics, Volume 35, Issue 1, 01 January 2019
graph neural networks, drug–target interaction prediction
Github - Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
- Masashi Tsubaki, Kentaro Tomii, Jun Sese
Bioinformatics, Volume 35, Issue 2, 15 January 2019
graph neural networks, interaction prediction
Github - Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
- A Transformer Model for Retrosynthesis
- Pavel Karpov, Guillaume Godin, Igor Tetko
ChemRxiv
graph neural networks, transformer, retrosynthesis, SMILES, USPTO
Github - Functional Transparency for Structured Data: a Game-Theoretic Approach
- Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
ICML 2019
graph neural networks, interpretability, transparency, decision trees - Interpretable Deep Learning in Drug Discovery
- Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner
arXiv 1903
graph neural networks, interpretability
Github - Analyzing Learned Molecular Representations for Property Prediction
- Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, Regina Barzilay
Journal of Chemical Information and Modeling, 2019, 59, 8
graph neural networks, benchmark results, quantum mechanics, physical chemistry, biophysics, physiology, directional message passing
Github - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong
Chemistry of Materials, 2019, 31, 9
graph neural networks, transfer learning
Github - A Bayesian Graph Convolutional Network for Reliable Prediction of Molecular Properties with Uncertainty Quantification
- Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Chemical Science, 2019, 36
graph neural networks, Bayesian inference, uncertainty
Github - Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
- Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim
Journal of Chemical Information and Modeling, 2019
graph neural networks, interaction prediction, 3D information - Molecule Property Prediction Based on Spatial Graph Embedding
- Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei
Journal of Chemical Information and Modeling, 2019
graph neural networks
Github - DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network
- Xiuming Li, Xin Yan, Qiong Gu, Huihao Zhou, Di Wu, Jun Xu
Journal of Chemical Information and Modeling, 2019, 59, 3
graph neural networks
Github - GNNExplainer: Generating Explanations for Graph Neural Networks
- Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec
NeurIPS 2019
graph neural networks, interpretability, information theory, node classification, link prediction, graph classification - Drug-Drug Adverse Effect Prediction with Graph Co-Attention
- Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang
arXiv 1905
graph neural networks, polypharmacy side effects - Pre-training Graph Neural Networks
- Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
arXiv 1905
graph neural networks, pre-training, self-supervised learning, protein function prediction, molecular property prediction - Graph Normalizing Flows
- Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
NeurIPS 2019
graph neural networks, invertible model, flow model, AE, QM9 - Retrosynthesis Prediction with Conditional Graph Logic Network
- Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song
NeurIPS 2019
graphical model, graph neural networks, retrosynthesis - Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
- Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He
AAAI 2019
graph neural networks, quantum mechanics - Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
- Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, Alpha A. Lee
ACS Central Science 2019, 5, 9
graph neural networks, reaction prediction, SMILES, machine translation, transformer - Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation
- Xianggen Liu, Pengyong Li, Sen Song
bioRXiv
retrosynthesis, GAT, attention, LSTM, USPTO - Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
- Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, Mingyue Zheng
Journal of Medicinal Chemistry 2019
graph neural networks, interpretability, adversarial, attention
Github - Structure-Based Function Prediction using Graph Convolutional Networks
- Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kyunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn Taylor, Ian M. Fisk, Ramnik J. Xavier, Rob Knight, Richard Bonneau
bioRXiv
graph neural networks, protein function prediction, Protein Data Bank, pre-trained language model, Bi-LSTM, interpretability - Molecule-Augmented Attention Transformer
- Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrz˛ebski
Graph Representation Learning Workshop at NeurIPS 2019
graph neural networks, property prediction, transformer - Learning Interaction Patterns from Surface Representations of Protein Structure
- Pablo Gainza, Freyr Sverrisson, Federico Monti, Emanuele Rodolà, Davide Boscaini, Michael Bronstein, Bruno E. Correia
Graph Representation Learning Workshop at NeurIPS 2019
graph neural networks, molecular surface, pocket similarity comparison, protein-protein interaction site prediction, prediction of interaction patterns - Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
- Benjamin Sanchez-Lengeling, Jennifer N Wei, Brian K Lee, Richard C Gerkin, Alán Aspuru-Guzik, and Alexander B Wiltschko
arXiv 1910
graph neural networks, property prediction, quantitative structure-odor relationship (QSOR) modeling, transfer learning - Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
- P. Gainza, F. Sverrisson, F. Monti, E. Rodol, D. Boscaini, M. M. Bronstein, B. E. Correia
Nature Methods 2019
graph neural networks, molecular surface interaction fingerprinting, geometric deep learning, protein pocket-ligand prediction, protein-protein interaction site prediction, ultrafast scanning of surfaces - A Deep Learning Approach to Antibiotic Discovery
- Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M.Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, James J. Collins
Cell
property prediction, inhibition of Escherichia coli, D-MPNN, graph neural networks, antibiotic discovery, drug repurpose, ensemble - Directional Message Passing for Molecular Graphs
- Johannes Klicpera, Janek Groß, Stephan Günnemann
ICLR 2020
graph neural networks, directional message passing, spherical Bessel functions, spherical harmonics, MD17, QM9, DimeNet
Github - InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
- Fan-Yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang
ICLR 2020
unsupervised learning, semi-supervised learning, information theory, graph representation learning, molecular property prediction - GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
- Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang
ICLR 2020
flow-based model, autoregressive, reinforcement learning, molecular property optimization, constrained property optimization, distribution learning - Deep Learning of Activation Energies
- Colin A. Grambow, Lagnajit Pattanaik, William H. Green
The Journal of Physical Chemistry Letters, 2020, 11
D-MPNN, molecular property prediction, reaction properties, template-free, activation energy - Molecule Property Prediction and Classification with Graph Hypernetworks
- Eliya Nachmani, Lior Wolf
arXiv 2002
hypernetworks, molecular property prediction, graph neural networks, NMP-Edge network, Invariant Graph Network, Graph Isomorphism Network, QM9, MUTAG, PROTEINS, PTC, NCI1, Open Quantum Materials Database (OQMD) - Molecule Attention Transformer
- Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrzębski
arXiv 2002
molecular property prediction, MoleculeNet, graph neural networks, transformers, pre-training, attention, interpretability, distance-based graph, dummy node - ProteinGCN: Protein model quality assessment using Graph Convolutional Networks
- Soumya Sanyal, Ivan Anishchenko, Anirudh Dagar, David Baker, Partha Talukdar
bioRxiv
graph neural networks, quality assessment, atom, residue, Rosetta-300k - Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
- Zeren Shui, George Karypis
ICDM 2020
graph neural networks, quantum chemistry, QM9, HMGNN, heterogeneous molecular graph, many-body interaction
Github - GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data
- Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang
NeurIPS 2020
graph neural networks, transformers, molecular property prediction, MoleculeNet, self-supervised learning, ZINC, ChEMBL, BBBP, SIDER, ClinTox, BACE, Tox21, ToxCast, FreeSolv, ESOL, Lipo, QM7, QM8 - TrimNet: learning molecular representation from triplet messages for biomedicine
- Pengyong Li, Yuquan Li, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song, Xiaojun Yao
Briefings in Bioinformatics, bbaa266
graph neural networks, MoleculeNet, interpretability, memory optimization - Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
- Shuo Zhang, Yang Liu, Lei Xie
NeurIPS 2020 Workshop on Machine Learning for Structural Biology & NeurIPS 2020 Workshop on Machine Learning for Molecules
graph neural networks, QM9, PDBBind, computational complexity
- GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
- Martin Simonovsky, Nikos Komodakis
arXiv 1802
graph neural networks, VAE, non-autoregressive, conditional generation, distribution-learning, QM9, ZINC - Junction Tree Variational Autoencoder for Molecular Graph Generation
- Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2018
graph neural networks, VAE, goal-directed optimization, ZINC
Github - NEVAE: A Deep Generative Model for Molecular Graphs
- Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez
AAAI 2019
graph neural networks, VAE, distribution learning, goal-directed optimization, ZINC, QM9
Github - Learning Deep Generative Models of Graphs
- Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia
arXiv 1803
graph neural networks, distribution learning, autoregressive, conditional generation, ChEMBL, ZINC - MolGAN: An implicit generative model for small molecular graphs
- Nicola De Cao, Thomas Kipf
arXiv 1805
graph neural networks, goal-directed optimization, non-autoregressive, RL, GAN, QM9
Github - Constrained Graph Variational Autoencoders for Molecule Design
- Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
NeurIPS 2018
graph neural networks, distribution-learning, goal-directed optimization, autoregressive, VAE, QM9, ZINC, CEPDB
Github - Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
- Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
NeurIPS 2018
graph neural networks, RL, GAN, MDP, goal-directed optimization, property targeting, ZINC
Github - Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
- Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer
Journal of Chemical Information and Modeling 2018, 58, 9
evaluation metric
Github - Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
- Tengfei Ma, Jie Chen, Cao Xiao
NeurIPS 2018
ConvNet, DeconvNet, non-autoregressive, distribution learning, QM9, ZINC - Molecular Hypergraph Grammar with Its Application to Molecular Optimization
- Multi-objective de novo drug design with conditional graph generative model
- Yibo Li, Liangren Zhang, Zhenming Liu
Journal of Cheminformatics, 10
graph neural networks, distribution-learning, auto-regressive, conditional generation, ChEMBL
Github - DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
- Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, Yoshua Bengio
arXiv 1811
graph neural networks, auto-regressive, goal-directed optimization, GAN, conditional generation, ZINC - Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
- Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
ICLR 2019
graph neural networks, VAE, WGAN, goal-directed optimization, ZINC
Github - A Generative Model For Electron Paths
- John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
ICLR 2019
graph neural networks, chemical reaction prediction, RL, MDP
Github - Graph Transformation Policy Network for Chemical Reaction Prediction
- Kien Do, Truyen Tran, Svetha Venkatesh
KDD 2019
graph neural networks, chemical reaction prediction - Mol-CycleGAN - a generative model for molecular optimization
- Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Michał Warchoł
arXiv 1902
graph neural networks, CycleGAN, goal-directed optimization - Molecular geometry prediction using a deep generative graph neural network
- Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho
arXiv 1904
graph neural networks, VAE, molecular conformation generation, energy function, conditional generation, QM9, COD, CSD
Github - Decoding Molecular Graph Embeddings with Reinforcement Learning
- Steven Kearnes, Li Li, Patrick Riley
arXiv 1904
graph neural networks, goal-directed optimization, MDP, VAE, QM9 - Likelihood-Free Inference and Generation of Molecular Graphs
- Sebastian Pölsterl, Christian Wachinger
arXiv 1905
graph neural networks, distribution learning, GAN, multi-graph, gumbel-softmax, QM9 - GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
- Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, Motoki Abe
arXiv 1905
graph neural networks, invertible model, flow model, distribution learning, goal-directed optimization, QM9, ZINC
Github - Scaffold-based molecular design using graph generative model
- Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn Kim
arXiv 1905
graph neural networks, scaffold, VAE, conditional generation, goal-directed optimization - A Model to Search for Synthesizable Molecules
- John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
NeurIPS 2019
graph neural networks, reaction prediction, distribution learning, goal-directed optimization, retrosynthesis - Discrete Object Generation with Reversible Inductive Construction
- Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
NeurIPS 2019
graph neural networks, distribution learning, Markov kernel, auto-regressive
Github - Generative models for graph-based protein design
- John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
NeurIPS 2019
graph neural networks, autoregressive, protein design, Rosetta
Github - Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules
- Wengong Jin, Regina Barzilay, Tommi Jaakkola
arXiv 1907
graph neural networks, goal-directed optimization, autoregressive, hierarchical, VAE, ZINC - Optimization of Molecules via Deep Reinforcement Learning
- Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick Riley
Scientific Reports 9
MDP, DQN, learning from scratch, autoregressive, goal-directed optimization
Github - Hierarchical Generation of Molecular Graphs using Structural Motifs
- Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2020
graph neural networks, generative models, hierarchical, VAE, graph motifs, multi-resolution
Github - A Graph to Graphs Framework for Retrosynthesis Prediction
- Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang
arXiv 2003
graph neural networks, retrosynthesis, reaction center identification, USPTO, conditional generative models - Unsupervised Attention-Guided Atom-Mapping
- Philippe Schwaller, Benjamin Hoover, Jean-Louis Reymond, Hendrik Strobelt, Teodoro Laino
ChemRxiv
graph neural networks, transformer, ALBERT, attention, atom mapping, self-supervised learning, reaction prediction, retrosynthesis, Hugging Face, masked language modeling - Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
- Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato
ICML 2020
graph neural networks, SchNet, reinforcement learning (RL), 3D, quantum chemistry, Cartesian coordinates, actor-critic, proximal policy optimization (PPO) - Multi-Objective Molecule Generation using Interpretable Substructures
- Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2020
multi-objective optimization, rationales, graph neural networks, accuracy, diversity, novelty, substructures, Monte Carlo tree search, reinforcement learning (RL), policy gradient - A Generative Model for Molecular Distance Geometry
- Gregor N. C. Simm, José Miguel Hernández-Lobato
ICML 2020
equilibrium states for many-body systems, molecular conformation, CVAE, mean maximum deviation distance, MPNN, multi-head attention, CONF17 - Improving Molecular Design by Stochastic Iterative Target Augmentation
- Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola
ICML 2020
self-training, property prediction model, data augmentation, iterative generation - Learning Graph Models for Template-Free Retrosynthesis
- Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay
ICML 2020 Workshop on Graph Representation Learning and beyond
retrosynthesis, graph neural networks, template-free