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Molecular docking is a pivotal process in drug discovery.
Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise
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Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4
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Luigi P Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2004 · 2004
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Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy
Richard A Friesner, Jay L Banks, Robert B Murphy, Thomas A Halgren, Jasna J Klicic, Daniel T Mainz, Matthew P Repasky, Eric H Knoll, Mee Shelley, Jason K Perry, et al · 2004
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Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods
Katarzyna Bernacki, Chakrapani Kalyanaraman, and Matthew P Jacobson. 2005 · 2005
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Niu Huang, Chakrapani Kalyanaraman, Katarzyna Bernacki, and Matthew P Jacobson. 2006 · 2006
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MolDock: a new technique for high-accuracy molecular docking
René Thomsen and Mikael H Christensen. 2006 · 2006
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Physics-based methods for studying protein-ligand interactions
Niu Huang and Matthew P Jacobson. 2007 · 2007
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PocketPicker: analysis of ligand binding-sites with shape descriptors
Martin Weisel, Ewgenij Proschak, and Gisbert Schneider. 2007 · 2007
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John A Capra, Roman A Laskowski, Janet M Thornton, Mona Singh, and Thomas A Funkhouser. 2009 · 2009
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Xue-wen Chen and Jong Cheol Jeong. 2009 · 2009
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Fpocket: an open source platform for ligand pocket detection
Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery. 2009 · 2009
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AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
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RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling
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Scheduled sampling for sequence prediction with recurrent neural networks
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Better informed distance geometry: using what we know to improve conformation generation
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Advances in free-energy-based simulations of protein folding and ligand binding
Alberto Perez, Joseph A Morrone, Carlos Simmerling, and Ken A Dill. 2016 · 2016
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Deep Learning Model for Flexible and Efficient Protein-Ligand Docking. In ICLR2022 Machine Learning for Drug Discovery
Matthew Masters, Amr H Mahmoud, Yao Wei, and Markus Alexander Lill. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Equibind: Geometric deep learning for drug binding structure prediction. In International Conference on Machine Learning . PMLR, 20503–20521
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. 2022 · 2022
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Accurate protein-ligand complex structure prediction using geometric deep learning
Junfeng Zhang, Kelei He, Tiejun Dong, et al · 2022
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E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
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Antonia Stank, Daria B Kokh, Jonathan C Fuller, and Rebecca C Wade. 2016 · 2016
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Sequence-based prediction of protein–peptide binding sites using support vector machine
Ghazaleh Taherzadeh, Yuedong Yang, Tuo Zhang, Alan Wee-Chung Liew, and Yaoqi Zhou. 2016 · 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 · 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 · 2017
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Software for molecular docking: a review
Nataraj S Pagadala, Khajamohiddin Syed, and Jack Tuszynski. 2017 · 2017
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes. 2017 · 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 · 2018
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Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, and Jian Tang. 2022a · 2022
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Direct Molecular Conformation Generation
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, and Tie-Yan Liu. 2022 · 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 · 2023
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The Discovery of Binding Modes Requires Rethinking Docking Generalization. In NeurIPS 2023 Generative AI and Biology (GenBio) Workshop
Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola. 2023a · 2023
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DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. In International Conference on Learning Representations (ICLR 2023)
Gabriele Corso, Bowen Jing, Regina Barzilay, Tommi Jaakkola, et al · 2023
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DiffDock-Site: A Novel Paradigm for Enhanced Protein-Ligand Predictions through Binding Site Identification. In NeurIPS 2023 Generative AI and Biology (GenBio) Workshop
Huanlei Guo, Song Liu, HU Mingdi, Yilun Lou, and Bingyi Jing. 2023 · 2023
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Lihang Liu, Donglong He, Xianbin Ye, Shanzhuo Zhang, Xiaonan Zhang, Jingbo Zhou, Jun Li, Hua Chai, Fan Wang, Jingzhou He, et al · 2023
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DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Wei Lu, Ji-Xian Zhang, Weifeng Huang, Ziqiao Zhang, Xiangyu Jia, Zhenyu Wang, Leilei Shi, Chengtao Li, Peter Wolynes, and Shuangjia Zheng. 2023 · 2023
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End-to-end protein–ligand complex structure generation with diffusion-based generative models
Shuya Nakata, Yoshiharu Mori, and Shigenori Tanaka. 2023 · 2023
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FABind: Fast and Accurate Protein-Ligand Binding. In Thirty-seventh Conference on Neural Information Processing Systems
Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, and Rui Yan. 2023 · 2023
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FlexiDock: Compositional diffusion models for flexible molecular docking
Zichen Wang, Balasubramaniam Srinivasan, Zhengyuan Shen, and George Karypis. 2023 · 2023
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Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Jiaxian Yan, Zaixi Zhang, Kai Zhang, and Qi Liu. 2023 · 2023
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Efficient and accurate large library ligand docking with KarmaDock
Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, et al · 2023
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EquiPocket: an E (3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Yang Zhang, Wenbing Huang, Zhewei Wei, Ye Yuan, and Zhaohan Ding. 2023a · 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 · 2023
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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