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The binding between proteins and ligands plays a crucial role in the realm of drug discovery.
Force fields for protein simulations
Jay W Ponder and David A Case · 2003
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The pdbbind database: methodologies and updates
Renxiao Wang, Xueliang Fang, Yipin Lu, Chao-Yie Yang, and Shaomeng Wang · 2005
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Autodock4 and autodocktools4: Automated docking with selective receptor flexibility
Garrett M Morris, Ruth Huey, William Lindstrom, Michel F Sanner, Richard K Belew, David S Goodsell, and Arthur J Olson · 2009
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Pedro J Ballester and John BO Mitchell · 2010
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott and Arthur J Olson · 2010
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Nnscore 2.0: a neural-network receptor–ligand scoring function
Jacob D Durrant and J Andrew McCammon · 2011
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Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking
Michael M Mysinger, Michael Carchia, John J Irwin, and Brian K Shoichet · 2012
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Biolip: a semi-manually curated database for biologically relevant ligand–protein interactions
Jianyi Yang, Ambrish Roy, and Yang Zhang · 2012
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho · 2013
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Izhar Wallach, Michael Dzamba, and Abraham Heifets · 2015
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Learning deep architectures for interaction prediction in structure-based virtual screening
Adam Gonczarek, Jakub M Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, and Michał J Walczak · 2016
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Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening
Rodrigo Quiroga and Marcos A Villarreal · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
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A systematic analysis of atomic protein–ligand interactions in the pdb
Renato Ferreira de Freitas and Matthieu Schapira · 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
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Deepdta: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Hidden bias in the dud-e dataset leads to misleading performance of deep learning in structure-based virtual screening
Lieyang Chen, Anthony Cruz, Steven Ramsey, Callum J Dickson, Jose S Duca, Viktor Hornak, David R Koes, and Tom Kurtzman · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Mostafa Karimi, Di Wu, Zhangyang Wang, and Yang Shen · 2019
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song · 2019
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Graph convolutional neural networks for predicting drug-target interactions
Wen Torng and Russ B Altman · 2019
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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Observing noncovalent interactions in experimental electron density for macromolecular systems: a novel perspective for protein–ligand interaction research
Kang Ding, Shiqiu Yin, Zhongwei Li, Shiju Jiang, Yang Yang, Wenbiao Zhou, Yingsheng Zhang, and Bo Huang · 2022
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Cosp: Co-supervised pretraining of pocket and ligand
Zhangyang Gao, Cheng Tan, Lirong Wu, and Stan Z Li · 2022
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Molecular geometry pretraining with se (3)-invariant denoising distance matching
Shengchao Liu, Hongyu Guo, and Jian Tang · 2022
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Onionnet: a multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction
Liangzhen Zheng, Jingrong Fan, and Yuguang Mu · 2019
Cited alongside, same era.
Deepcda: deep cross-domain compound–protein affinity prediction through lstm and convolutional neural networks
Karim Abbasi, Parvin Razzaghi, Antti Poso, Massoud Amanlou, Jahan B Ghasemi, and Ali Masoudi-Nejad · 2020
Cited alongside, same era.
Monn: A multi-objective neural network for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins
Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, and Jianyang Zeng · 2020
Cited alongside, same era.
Atom3d: Tasks on molecules in three dimensions
Raphael JL Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, et al · 2020
Cited alongside, same era.
Predicting drug–protein interaction using quasi-visual question answering system
Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, and Yuedong Yang · 2020
Cited alongside, same era.
Binding affinity prediction by pairwise function based on neural network
Fangqiang Zhu, Xiaohua Zhang, Jonathan E Allen, Derek Jones, and Felice C Lightstone · 2020
Cited alongside, same era.
Prottrans: Toward understanding the language of life through self-supervised learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2021
Cited alongside, same era.
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
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Dtitr: End-to-end drug–target binding affinity prediction with transformers
Nelson RC Monteiro, José L Oliveira, and Joel P Arrais · 2022
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Pignet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, and Woo Youn Kim · 2022
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Smt-dta: Improving drug-target affinity prediction with semi-supervised multi-task training
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, and Tie-Yan Liu · 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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Mole-bert: Rethinking pre-training graph neural networks for molecules
Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z Li · 2022
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Attentionsitedti: an interpretable graph-based model for drug-target interaction prediction using nlp sentence-level relation classification
Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Elayaraja Kolanthai, Craig J Neal, Sudipta Seal, and Ozlem Ozmen Garibay · 2022
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The realm of unconventional noncovalent interactions in proteins: Their significance in structure and function
Vishal Annasaheb Adhav and Kayarat Saikrishnan · 2023
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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Efficient self-supervised learning with contextualized target representations for vision, speech and language
Alexei Baevski, Arun Babu, Wei-Ning Hsu, and Michael Auli · 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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