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Molecular representation learning has attracted much attention recently.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
David Weininger. 1988 · 1988
Earlier work this paper cites.
Reoptimization of MDL Keys for Use in Drug Discovery
Joseph L. Durant, Burton A. Leland, Douglas R. Henry, and James G. Nourse. 2002 · 2002
Earlier work this paper cites.
Quaternions in molecular modeling
Charles FF Karney. 2007 · 2007
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML , Vol. 37. Lille, France, 448–456
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Neural Message Passing for Quantum Chemistry. In ICML . 1263–1272
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
Earlier work this paper cites.
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit. 2017 · 2017
Earlier work this paper cites.
SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions. In NeurIPS (Long Beach, California, USA). Curran Associates Inc., Red Hook, NY, USA, 992–1002
K. T. Schütt, P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, and K.-R. Müller. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need. In NeurIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
Kedi Wu and Guo-Wei Wei. 2018 · 2018
Earlier work this paper cites.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018 · 2018
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda. 2019 · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Earlier work this paper cites.
Molecular property prediction: A multilevel quantum interactions modeling perspective. In AAAI , Vol. 33. 1052–1060
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He. 2019 · 2019
Earlier work this paper cites.
Molecular Geometry Prediction using a Deep Generative Graph Neural Network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho. 2019 · 2019
Earlier work this paper cites.
Smiles-bert: large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics . 429–436
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. 2019 · 2019
Earlier work this paper cites.
How Powerful are Graph Neural Networks?. In ICLR 2019
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Cited alongside, same era.
Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli. 2020 · 2020
Cited alongside, same era.
Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. 2020 · 2020
Cited alongside, same era.
Are 2D fingerprints still valuable for drug discovery?
Kaifu Gao, Duc Duy Nguyen, Vishnu Sresht, Alan M. Mathiowetz, Meihua Tu, and Guo-Wei Wei. 2020 · 2020
Cited alongside, same era.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020a · 2020
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021 · 2021
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Deep learning identifies synergistic drug combinations for treating COVID-19
Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay. 2021 · 2021
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Predicting Molecular Conformation via Dynamic Graph Score Matching. In NeurIPS , Vol. 34
Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang. 2021 · 2021
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Zero-Shot Text-to-Image Generation. In ICML , Vol. 139. 8821–8831
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra. In ICML
Kristof Schütt, Oliver Unke, and Michael Gastegger. 2021 · 2021
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Cited alongside, same era.
Directional Message Passing for Molecular Graphs. In ICLR
Johannes Klicpera, Janek Groß, and Stephan Günnemann. 2020 · 2020
Cited alongside, same era.
FLAG: Adversarial Data Augmentation for Graph Neural Networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein. 2020 · 2020
Cited alongside, same era.
spyrmsd: symmetry-corrected RMSD calculations in Python
Rocco Meli and Philip C. Biggin. 2020 · 2020
Cited alongside, same era.
Self-Supervised Graph Transformer on Large-Scale Molecular Data. In NeurIPS , Vol. 33. 12559–12571
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, and Junzhou Huang. 2020 · 2020
Cited alongside, same era.
Heterogeneous molecular graph neural networks for predicting molecule properties. In ICDM . IEEE, 492–500
Zeren Shui and George Karypis. 2020 · 2020
Cited alongside, same era.
A Generative Model for Molecular Distance Geometry. In ICML , Vol. 119. PMLR, 8949–8958
Gregor Simm and Jose Miguel Hernandez-Lobato. 2020 · 2020
Cited alongside, same era.
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-Ackermann, et al · 2020
Cited alongside, same era.
Later among the works it cites.
Artificial Intelligence in Bioinformatics
Hari Om Sharan. 2021 · 2021
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Learning gradient fields for molecular conformation generation. In ICML . PMLR, 9558–9568
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang. 2021 · 2021
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3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò. 2021 · 2021
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3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò. 2021 · 2021
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MolCLR: molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. 2021 · 2021
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Do Transformers Really Perform Badly for Graph Representation?. In Advances in Neural Information Processing Systems
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
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Point transformer. In ICCV . 16259–16268
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. 2021 · 2021
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Dual-view molecule pre-training
Jinhua Zhu, Yingce Xia, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2021 · 2021
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Pre-training Molecular Graph Representation with 3D Geometry. In ICLR
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. 2022 · 2022
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GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. In ICLR
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang. 2022 · 2022
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Direct molecular conformation generation
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, and Tie-Yan Liu. 2022 · 2022
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