👏Geometry Deep Learning for Drug Discovery and Life Science

💡Related to Geometric Deep Learning for Drug discovery and Life Science.

💡Related review paper has been accepted by Expert System With Applications [Paper].

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Recommendations and references

Generative AI and Deep Learning for molecular/drug design
https://github.com/AspirinCode/papers-for-molecular-design-using-DL

List of papers about Proteins Design using Deep Learning
https://github.com/Peldom/papers_for_protein_design_using_DL

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•  Geometry Deep Learning for Drug Discovery and Life Science
  • Recommendations and references
  • Menu
  • Reviews
  • Datasets and Benchmarks
    • Datasets
    • Benchmarks
  • Molecular property prediction
  • Intermolecular interaction
    • Binding site prediction
    • Binding affinity prediction
  • Molecular design
    • Ligand-based drug design
    • Structure-based drug design
    • Macromolecular design
  • Molecular conformation generation
    • Molecular-based conformation generation
    • Complex-based conformation generation
    • Protein-based conformation generation
  • Molecular 3D pretraining

Reviews

• A Systematic Survey of Chemical Pre-trained Models [IJCAI 2023]
  [Paper]

• Structure-based drug design with geometric deep learning[2023]
  [Paper]

• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design[2022]
  [Paper]

• Geometrically Equivariant Graph Neural Networks: A Survey[2022]
  [Paper]

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges[2021]
  [Paper]

• Geometric deep learning on molecular representations[2021]
  [Paper]

Datasets and Benchmarks

Datasets

QM dataset

http://quantum-machine.org/datasets/

Qmugs

https://www.nature.com/articles/s41597-022-01390-7

GEOM

https://www.nature.com/articles/s41597-022-01288-4

OC20

https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md

PDBBIND

http://pdbbind.org.cn/

DIPS

https://github.com/drorlab/DIPS

PCQM4Mv2

https://ogb.stanford.edu/docs/lsc/pcqm4mv2/

CrossDocked2020

https://github.com/gnina/models/tree/master/data

Benchmarks

Leaderboard for PCQM4Mv2

https://ogb.stanford.edu/docs/lsc/leaderboards/#pcqm4mv2

Molecular property prediction

• A universal framework for accurate and efficient geometric deep learning of molecular systems [Nature Scientific Reports 2023]
  Shuo Zhang, Yang Liu, Lei Xie.
  Paper | code

• Uncertainty Estimation for Molecules: Desiderata and Methods [ICML 2023]
  Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann.
  Paper

• Quantum 3D Graph Learning with Applications to Molecule Embedding [ICML 2023]
  Ge Yan, Huaijin Wu, Junchi Yan.
  Paper

• Geometry-Complete Perceptron Networks for 3D Molecular Graphs [AAAI 2023]
  Alex Morehead, Jianlin Cheng.
  Paper | code

• Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs [AAAI 2023]
  Fang Wu, Dragomir Radev, Stan Z. Li.
  Paper | code

• ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs [NeurIPS 2022]
  Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji.
  Paper | code

• Recipe for a General, Powerful, Scalable Graph Transformer [NeurIPS 2022]
  Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini.
  Paper | code

• GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction [2022]
  Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Ladislav Rampášek, Dominique Beaini.
  Paper | code

• Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets [2022]
  Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu.
  Paper | code

• Spherical message passing for 3d graph networks [ICLR 2022]
  Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, Shuiwang Ji.
  Paper | code

• TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials [ICLR 2022]
  Philipp Thölke, Gianni De Fabritiis.
  Paper | code

• Equivariant Graph Mechanics Networks with Constraints [ICLR 2022]
  Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang.
  Paper | code

• Geometric and Physical Quantities Improve E(3) Equivariant Message Passing [ICLR 2022]
  Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling.
  Paper

• GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction [AAAI 2022]
  Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong.
  Paper | code

• Geometric Transformer for End-to-End Molecule Properties Prediction [IJCAI 2022]
  Yoni Choukroun, Lior Wolf.
  Paper | code

• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials [Nature Communications 2021]
  Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky.
  Paper | code

• Equivariant message passing for the prediction of tensorial properties and molecular spectra [ICML 2021]
  Kristof T. Schütt, Oliver T. Unke, Michael Gastegger.
  Paper

• E (n) equivariant graph neural networks [ICML 2021]
  Victor Garcia Satorras, Emiel Hoogeboom, Max Welling.
  Paper | code

• GemNet: Universal Directional Graph Neural Networks for Molecules [ICLR 2021]
  Johannes Gasteiger, Florian Becker, Stephan Günnemann.
  Paper | code

• Spatial Graph Convolutional Networks [ICONIP 2020]
  Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka.
  Paper | code

• Directional Message Passing for Molecular Graphs [ICLR 2020]
  Johannes Gasteiger, Janek Groß, Stephan Günnemann.
  Paper | code

• Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties [2020]
  Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé.
  Paper | code

• Cormorant: Covariant molecular neural networks [NeurIPS 2019]
  Brandon Anderson, Truong-Son Hy, Risi Kondor.
  Paper | code

• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds [2018]
  Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley.
  Paper | code

• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions [NeurIPS 2017]
  Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller.
  Paper | code

Intermolecular interaction

Binding site prediction

• ScanNet: an interpretable geometric deeplearning model for structure-based protein binding site prediction [Nature Methods 2022]
  Tubiana, Jérôme, Schneidman-Duhovny, Dina, Wolfson, Haim J.
  Paper | code

• Geometric Transformers for Protein Interface Contact Prediction [ICLR 2022]
  Alex Morehead, Chen Chen, Jianlin Cheng.
  Paper | code

• Fast end-to-end learning on protein surfaces [CVPR 2021]
  Freyr Sverrisson, Jean Feydy, Bruno E Correia, Michael M Bronstein.
  Paper | code

• Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning [Nature Methods 2019]
  Gainza et al.
  Paper | code

• DeepSite: protein-binding site predictor using 3D-convolutional neural networks [Bioinformatics 2017]
  Jiménez et al.
  Paper

Binding affinity prediction

• A universal framework for accurate and efficient geometric deep learning of molecular systems [Nature Scientific Reports 2023]
  Shuo Zhang, Yang Liu, Lei Xie.
  Paper | code

• Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN). [JPCL 2023]
  Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Calvin Yu-Chian Chen.
  Paper | code

• Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation [JCIM 2019]
  Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim.
  Paper | code

• Graph Convolutional Neural Networks for Predicting Drug-Target Interactions [JCIM 2019]
  Wen Torng, Russ B. Altman.
  Paper

• PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions [Chemical Science 2020]
  Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim.
  Paper | code

• Multi-Scale Representation Learning on Proteins [NeurIPS 2021]
  Vignesh Ram Somnath, Charlotte Bunne, Andreas Krause.
  Paper | code

• InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions [IMC 2021]
  Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu, Dongsheng Cao, Tingjun Hou.
  Paper | code

• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [SIGKDD 2021]
  Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong.
  Paper

• AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery [2021]
  Izhar Wallach, Michael Dzamba, Abraham Heifets.
  Paper

• KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks [JCIM 2018]
  José Jiménez et al.
  Paper

Molecular design

Ligand-based drug design

• DiffMol: 3D Structured Molecule Generation with Discrete Denoising Diffusion Probabilistic Models [ICML 2023 Workshop]
  Weitong Zhang, Xiaoyun Wang, Justin Smith, Joe Eaton, Brad Rees, Quanquan Gu.
  Paper

• MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation [ICML 2023]
  Xingang Peng, Jiaqi Guan, qiang liu, Jianzhu Ma.
  Paper | code

• Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D [ICML 2023]
  Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, Yanyan Lan.
  Paper | code

• Geometric Latent Diffusion Models for 3D Molecule Generation [ICML 2023]
  Minkai Xu, Alexander Powers, Ron Dror, Stefano Ermon, Jure Leskovec.
  Paper | code

• Geometry-Complete Diffusion for 3D Molecule Generation [ICLR 2023]
  Alex Morehead, Jianlin Cheng.
  Paper | code

• MDM: Molecular Diffusion Model for 3D Molecule Generation [AAAI 2023]
  Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong
  Paper | code

• MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation [ECML 2023]
  Clement Vignac, Nagham Osman, Laura Toni, Pascal Frossard
  Paper | code

• MUDiff: Unified Diffusion for Complete Molecule Generation [2023]
  Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup.
  Paper

• SILVR: Guided Diffusion for Molecule Generation [2023]
  Nicholas T. Runcie, Antonia S. J. S. Mey.
  Paper | code

• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [2023]
  Nicholas T. Runcie, Antonia S. J. S. Mey.
  Paper | code

• Hyperbolic Graph Diffusion Model for Molecule Generation [2023]
  Lingfeng Wen, Xian Wei
  Paper

• Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models [2023]
  Ziqi Chen, Bo Peng, Srinivasan Parthasarathy, Xia Ning
  Paper

• EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency [2023]
  Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
  Paper

• Torsional Diffusion for Molecular Conformer Generation [NeurIPS 2022]
  Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
  Paper | code

• Diffusion-based Molecule Generation with Informative Prior Bridges [NeurIPS 2022]
  Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu
  Paper

• Equivariant Diffusion for Molecule Generation in 3D [ICML 2022]
  Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling.
  Paper | code

• E(n) Equivariant Normalizing Flows [NeurIPS 2021]
  Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling.
  Paper | code

• Symmetry-Aware Actor-Critic for 3D Molecular Design [ICLR 2021]
  Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato.
  Paper | code

• Reinforcement Learning for Molecular Design Guided by Quantum Mechanics [ICML 2020]
  Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato.
  Paper | code

• An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch [ICLR 2022]
  Youzhi Luo, Shuiwang Ji.
  Paper | code

• Inverse design of 3d molecular structures with conditional generative neural networks [Nature Communications 2021]
  Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt.
  Paper | code

• 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds [JPCB 2021]
  Rajendra P Joshi, Niklas W A Gebauer, Mridula Bontha, Mercedeh Khazaieli, Rhema M James, James B Brown, Neeraj Kumar.
  Paper | code

• Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules [NeurIPS 2019]
  Niklas W. A. Gebauer, Michael Gastegger, Kristof T.
  Paper | code

Structure-based drug design

• Learning Subpocket Prototypes for Generalizable Structure-based Drug Design [ICML 2023]
  ZAIXI ZHANG, Qi Liu.
  Paper | code

• DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [ICML 2023]
  Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu.
  Paper | code

• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [ICLR 2023]
  Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma.
  Paper | code

• Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration [2023]
  Haitao Lin, Yufei Huang, Haotian Zhang, Lirong Wu, Siyuan Li, Zhiyuan Chen, Stan Z. Li Paper

• Structure-based Drug Design with Equivariant Diffusion Models [2022]
  Arne Schneuing et al.
  Paper | code

• Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design [2022]
  Ilia Igashov et al.
  Paper | code

• Fragment-Based Ligand Generation Guided By Geometric Deep Learning On Protein-Ligand Structure [2022]
  Alexander Powers, Helen Yu, Patricia Suriana, Ron Dror.
  Paper | code

• Generating 3D Molecules for Target Protein Binding [ICML 2022]
  Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji.
  Paper | code

• 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design [ICML 2022]
  Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang.
  Paper | code

• Structure-based de novo drug design using 3D deep generative models [Chemical Science 2021]
  Yibo Li, Jianfeng Pei, Luhua Lai.
  Paper

• A 3D generative model for structure-based drug design [NeurIPS 2021]
  Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng
  Paper | code

• Generating 3d molecular structures conditional on a receptor binding site with deep generative models [Chemical Science 2021]
  Tomohide Masuda, Matthew Ragoza, David Ryan Koes.
  Paper | code

Macromolecular design

• End-to-End Full-Atom Antibody Design [ICML 2023]
  Xiangzhe Kong, Wenbing Huang, Yang Liu.
  Paper | code

• AbODE: Ab initio antibody design using conjoined ODEs [ICML 2023]
  Yogesh Verma, Markus Heinonen, Vikas Garg.
  Paper

• SE(3) diffusion model with application to protein backbone generation [ICML 2023]
  Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola.
  Paper | code

• Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds [ICML 2023]
  Yeqing Lin, Mohammed AlQuraishi.
  Paper | code

• Chemically Transferable Generative Backmapping of Coarse-Grained Proteins [ICML 2023]
  Soojung Yang, Rafael Gomez-Bombarelli.
  Paper | code

• Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer [2023]
  Cheng Tan, Zhangyang Gao, Stan Z. Li.
  Paper

• Conditional Antibody Design as 3D Equivariant Graph Translation [ICLR 2023]
  Xiangzhe Kong, Wenbing Huang, Yang Liu.
  Paper | code

• Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models [NIPS 2022]
  Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma.
  Paper | code

• Protein Sequence and Structure Co-Design with Equivariant Translation [ICLR 2023]
  Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang.
  Paper

• Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design [ICLR 2022 spotlight]
  Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola.
  Paper | code

Molecular conformation generation

Molecular-based conformation generation

• GeoDiff:A Geometric Diffusion Model for Molecular Conformation Generation [ICLR 2022]
  Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang.
  Paper | code

• Learning Neural Generative Dynamics for Molecular Conformation Generation [ICLR 2021]
  Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang.
  Paper | code

• An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming [ICML 2021]
  Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang.
  Paper | code

• A Generative Model for Molecular Distance Geometry [ICML 2020]
  Gregor N. C. Simm, José Miguel Hernández-Lobato.
  Paper | code

• Learning Gradient Fields for Molecular Conformation Generation [ICML 2021]
  Chence Shi, Shitong Luo, Minkai Xu, Jian Tang.
  Paper | code

• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [NeurIPS 2021]
  Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green, Tommi S. Jaakkola.
  Paper | code

• Predicting Molecular Conformation via Dynamic Graph Score Matching [NeurIPS 2021]
  Shitong Luo, Chence Shi, Minkai Xu, Jian Tang.
  Paper

• Direct Molecular Conformation Generation [2022]
  Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu.
  Paper | code

• Molecular Geometry Prediction using a Deep Generative Graph Neural Network [Scientific Reports 2019]
  Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho.
  Paper | code

Complex-based conformation generation

• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking [2022]
  Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S Jaakkola.
  Paper

• TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction [NeurIPS 2022]
  Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, Shuangjia Zheng.
  Paper | code

• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction [ICML 2022]
  Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, T Jaakkola.
  Paper | code

• A geometric deep learning approach to predict binding conformations of bioactive molecules [JCIM 2021]
  Arne Schneuing et al.
  Paper | code

• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking [ICLR 2022]
  Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, Andreas Krause.
  Paper | code

• DeepBSP—a machine learning method for accurate prediction of protein–ligand docking structures [2021]
  Jingxiao Bao, Xiao He,* and John Z. H. Zhang*.
  Paper | code

Protein-based conformation generation

• EigenFold: Generative Protein Structure Prediction with Diffusion Models [ICLR 2023 workshop]
  Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi Jaakkola.
  Paper | code

• Highly accurate protein structure prediction with AlphaFold [Nature]
  John et al.
  Paper | code

• Learning Hierarchical Protein Representations via Complete 3D Graph Networks [ICLR 2023]
  Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, Shuiwang Ji.
  Paper | code

• ScanNet: an interpretable geometric deeplearning model for structure-based protein binding site prediction [Nature communications 2021]
  Nicolas Renaud, Cunliang Geng, Sonja Georgievska, Francesco Ambrosetti, Lars Ridder, Dario F. Marzella, Manon F. Réau, Alexandre M. J. J. Bonvin & Li C. Xue .
  Paper | code

• Geometric deep learning of RNA structure [Science 2021]
  RAPHAEL J. L. TOWNSHEND, STEPHAN EISMANN, ANDREW M. WATKINS, RAMYA RANGAN, MARIA KARELINA, RHIJU DAS, AND RON O. DROR.
  Paper

• Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures [ICLR 2021]
  Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, Timo Ropinski.
  Paper | code

• Learning from Protein Structure with Geometric Vector Perceptrons [ICLR 2021]
  Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J.L. Townshend, Ron Dror.
  Paper | code

• SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects [Nature communications 2021]
  Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda & Klaus-Robert Müller .
  Paper | code

Molecular 3D pretraining

• Protein Representation Learning by Geometric Structure Pretraining [ICLR 2023]
  Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang.
  Paper | code

• Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding [Advanced Science 2022]
  Fang Wu, Shuting Jin, Yinghui Jiang, Xurui Jin, Bowen Tang, Zhangming Niu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, Stan Z. Li.
  Paper | code

• Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures [AAAI 2022]
  Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, Junzhou Huang.
  Paper

• Fractional Denoising for 3D Molecular Pre-training [ICML 2023] Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Weiying Ma. Paper | code

• A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining [ICML 2023] Shengchao Liu, weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang
  Paper | code

• Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules [ICLR 2023] Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z. Li.
  Paper | code

• LagNet: Deep Lagrangian Mechanics for Plug-and-Play Molecular Representation Learning [AAAI 2023]
  Chunyan Li, JunfengYao, Jinsong Su, Zhaoyang Liu, Xiangxiang Zeng, Chenxi Huang.
  Paper

• Transformer-M: One Transformer Can Understand Both 2D & 3D Molecular Data [ICLR 2023]
  Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He.
  Paper | code

• Uni-Mol: A Universal 3D Molecular Representation Learning Framework [ICLR 2023]
  Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke.
  Paper | code

• Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching [ICLR 2023]
  Shengchao Liu, Hongyu Guo, Jian Tang.
  Paper | code

• Pre-training via Denoising for Molecular Property Prediction [ICLR 2023]
  Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin.
  Paper | code

• Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs [AAAI 2023]
  Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, Yang Liu.
  Paper | code

• Zero-Shot 3D Drug Design by Sketching and Generating [NeurIPS 2022]
  Siyu Long, Yi Zhou, Xinyu Dai, Hao Zhou.
  Paper | code

• Pre-training Molecular Graph Representation with 3D Geometry [ICLR 2022]
  Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang.
  Paper | code

• Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond [ICLR 2022]
  Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, Peter Battaglia.
  Paper

• Geometry-enhanced molecular representation learning for property prediction [NMI 2022]
  Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu & Haifeng Wang.
  Paper | code

• 3D Infomax improves GNNs for Molecular Property Prediction [ICML 2022]
  Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò.
  Paper | code

• Unified 2D and 3D Pre-Training of Molecular Representations [KDD 2022]
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