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Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery.
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Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
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Martin Weisel, Ewgenij Proschak, and Gisbert Schneider · 2007
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Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3d structure
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Fpocket: an open source platform for ligand pocket detection
Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery · 2009
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Oleg Trott and Arthur J Olson · 2010
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Towards a knowledge-based human protein atlas
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Better informed distance geometry: using what we know to improve conformation generation
Sereina Riniker and Gregory A Landrum · 2015
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Professor forcing: A new algorithm for training recurrent networks
Alex M Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C Courville, and Yoshua Bengio · 2016
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Protein binding pocket dynamics
Antonia Stank, Daria B Kokh, Jonathan C Fuller, and Rebecca C Wade · 2016
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Deepsite: protein-binding site predictor using 3d-convolutional neural networks
José Jiménez, Stefan Doerr, Gerard Martínez-Rosell, Alexander S Rose, and Gianni De Fabritiis · 2017
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Forging the basis for developing protein–ligand interaction scoring functions
Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Rcsb protein data bank: powerful new tools for exploring 3d structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences
Stephen K Burley, Charmi Bhikadiya, Chunxiao Bi, Sebastian Bittrich, Li Chen, Gregg V Crichlow, Cole H Christie, Kenneth Dalenberg, Luigi Di Costanzo, Jose M Duarte, et al · 2021
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Independent se (3)-equivariant models for end-to-end rigid protein docking
Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause · 2021
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Equivariant graph neural networks for 3d macromolecular structure
Bowen Jing, Stephan Eismann, Pratham N Soni, and Ron O Dror · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Radoslav Krivák and David Hoksza · 2018
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Castp 3.0: computed atlas of surface topography of proteins
Wei Tian, Chang Chen, Xue Lei, Jieling Zhao, and Jie Liang · 2018
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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2019
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Gnina 1.0: molecular docking with deep learning
Andrew T McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, and David Ryan Koes · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona, and Jörg Kurt Wegner · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Deep scoring neural network replacing the scoring function components to improve the performance of structure-based molecular docking
Lijuan Yang, Guanghui Yang, Xiaolong Chen, Qiong Yang, Xiaojun Yao, Zhitong Bing, Yuzhen Niu, Liang Huang, and Lei Yang · 2021
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2022
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Conditional antibody design as 3d equivariant graph translation
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
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Deep learning model for flexible and efficient protein-ligand docking
Matthew Masters, Amr H Mahmoud, Yao Wei, and Markus Alexander Lill · 2022
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Equibind: Geometric deep learning for drug binding structure prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola · 2022
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E3bind: An end-to-end equivariant network for protein-ligand docking
Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, and Jian Tang · 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, et al · 2022
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The impact of large language models on scientific discovery: a preliminary study using gpt-4
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
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