/DL4MolecularGraph

Literature of deep learning for graphs in Chemistry and Biology

Deep Learning for Graphs in Chemistry and Biology

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.

1   Review

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

2   Benchmark and Dataset

2.1   Discriminative Models

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

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

2.2   Generative Models

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

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

3   Discriminative Models

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

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

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

Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola
ICML 2017
graph neural networks

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

Learning Graph-Level Representation for Drug Discovery

Junying Li, Deng Cai, Xiaofei He
arXiv 1709
graph neural networks

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

Protein Interface Prediction Using Graph Convolutional Networks

Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
NeurIPS 2017
graph neural networks, protein interface prediction

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
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

Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
arXiv 1805
graph neural networks

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

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
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

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

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis

Katsuhiko Ishiguro, Shin-ichi Maeda, Masanori Koyama
arXiv 1902
graph neural networks

A Transformer Model for Retrosynthesis

Pavel Karpov, Guillaume Godin, Igor Tetko
ChemRxiv
graph neural networks, transformer, retrosynthesis, SMILES, USPTO

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

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

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

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

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

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

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

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
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

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

4   Generative Models

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

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

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

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

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

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

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

Hiroshi Kajino
ICML 2019
grammar, VAE, hypergraph, goal-directed 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

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

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

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

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

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

Generative models for graph-based protein design

John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
NeurIPS 2019
graph neural networks, autoregressive, protein design, Rosetta

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

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

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