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Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
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SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R. Ueda · 1911
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The whim theory: New 3d molecular descriptors for qsar in environmental modelling
R. Todeschini and P. Gramatica · 1997
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2003
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Jean-Louis Reymond · 2012
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Improving the human hazard characterization of chemicals: a tox21 update
Raymond R Tice, Christopher P Austin, Robert J Kavlock, and John R Bucher · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, O. Dral, P., M. Rupp, and O. Anatole von Lilienfeld · 2014
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Pubchem substance and compound databases
Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Drug-induced adverse events prediction with the lincs l1000 data
Zichen Wang, Neil R Clark, and Avi Ma’ayan · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry
Maho Nakata and Tomomi Shimazaki · 2017
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A next generation connectivity map: L1000 platform and the first 1,000,000 profiles
Aravind Subramanian, Rajiv Narayan, Steven M Corsello, David D Peck, Ted E Natoli, Xiaodong Lu, Joshua Gould, John F Davis, Andrew A Tubelli, Jacob K Asiedu, et al · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Stellargraph machine learning library
CSIRO’s Data61 · 2018
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MoleculeNet: A benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
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Guacamol: Benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin H. S. Segler, and Alain C. Vaucher · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Dissecting the graphcore ipu architecture via microbenchmarking, 2019
Zhe Jia, Blake Tillman, Marco Maggioni, and Daniele Paolo Scarpazza · 2019
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Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability
Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, and Ola Engkvist · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Jraph: A library for graph neural networks in jax., 2020
Jonathan Godwin*, Thomas Keck*, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Veličković, and Alvaro Sanchez-Gonzalez · 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 · 2020
Cited alongside, same era.
Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle · 2020
Cited alongside, same era.
Pubchemqc pm6: Data sets of 221 million molecules with optimized molecular geometries and electronic properties
Extreme acceleration of graph neural network-based prediction models for quantum chemistry, 2022
Hatem Helal, Jesun Firoz, Jenna Bilbrey, Mario Michael Krell, Tom Murray, Ang Li, Sotiris Xantheas, and Sutanay Choudhury · 2022
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nabladft: Large-scale conformational energy and hamiltonian prediction benchmark and dataset
Kuzma Khrabrov, Ilya Shenbin, Alexander Ryabov, Artem Tsypin, Alexander Telepov, Anton Alekseev, Alexander Grishin, Pavel Strashnov, Petr Zhilyaev, Sergey Nikolenko, et al · 2022
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Pangu drug model: learn a molecule like a human
Xinyuan Lin, Chi Xu, Zhaoping Xiong, Xinfeng Zhang, Ningxi Ni, Bolin Ni, Jianlong Chang, Ruiqing Pan, Zidong Wang, Fan Yu, et al · 2022
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One transformer can understand both 2D & 3D molecular data
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maho Nakata, Tomomi Shimazaki, Masatomo Hashimoto, and Toshiyuki Maeda · 2020
Cited alongside, same era.
Transfer learning or self-supervised learning? a tale of two pretraining paradigms
Xingyi Yang, Xuehai He, Yuxiao Liang, Yue Yang, Shanghang Zhang, and Pengtao Xie · 2020
Cited alongside, same era.
Design space for graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2020
Cited alongside, same era.
Directional graph networks
Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, and Pietro Liò · 2021
Cited alongside, same era.
Weisfeiler and lehman go cellular: Cw networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Lio, Guido F Montufar, and Michael Bronstein · 2021
Cited alongside, same era.
Open catalyst 2020 (oc20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al · 2021
Cited alongside, same era.
Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2021
Cited alongside, same era.
Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
Cited alongside, same era.
Oscar Méndez-Lucio, Christos Nicolaou, and Berton Earnshaw · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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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 · 2022
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Does gnn pretraining help molecular representation?
Ruoxi Sun, Hanjun Dai, and Adams Wei Yu · 2022
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Exposing the limitations of molecular machine learning with activity cliffs
Derek van Tilborg, Alisa Alenicheva, and Francesca Grisoni · 2022
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Pre-training of equivariant graph matching networks with conformation flexibility for drug binding
Fang Wu, Shuting Jin, Yinghui Jiang, Xurui Jin, Bowen Tang, Zhangming Niu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, and Stan Z Li · 2022
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Tensor programs V: tuning large neural networks via zero-shot hyperparameter transfer
Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, and Jianfeng Gao · 2022
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, and Jonathan Godwin · 2022
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Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal A. C. Xhonneux, Meng Qu, and Jian Tang · 2022
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Cogdl: A comprehensive library for graph deep learning
Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, and Jie Tang · 2023
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Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, et al · 2023
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GPS++: Reviving the art of message passing for molecular property prediction
Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew Fitzgibbon, Shenyang Huang, et al · 2023
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Generating QM1b with PySCF ipu \text{PySCF}_{\text{ipu}}
Alexander Mathiasen, Hatem Helal, Kerstin Klaeser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew William Fitzgibbon, and Dominic Masters · 2023
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Context-enriched molecule representations improve few-shot drug discovery
Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, and Günter Klambauer · 2023
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Misato-machine learning dataset of protein-ligand complexes for structure-based drug discovery
Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Stefan Kesselheim, Marie Piraud, Fabian J Theis, Michael Sattler, and Grzegorz M Popowicz · 2023
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A systematic survey of chemical pre-trained models
Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, and Stan Z Li · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
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Large-scale comparison of machine learning methods for drug target prediction on chembl
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter · 2041
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Robin Winter, Floriane Montanari, Frank Noé, and Djork-Arné Clevert · 2041
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