Fetching the paper…
Reading the bibliography…
In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs.
Binding moad (mother of all databases)
Liegi Hu, Mark L Benson, Richard D Smith, Michael G Lerner, and Heather A Carlson · 2005
Earlier work this paper cites.
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
Earlier work this paper cites.
Comparative assessment of scoring functions: the casf-2016 update
Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, and Renxiao Wang · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Highly accurate quantum chemical property prediction with uni-mol+, 2023
Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, and Guolin Ke · 2023
Cited alongside, same era.
Generalized biomolecular modeling and design with rosettafold all-atom
Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A Sutherland, Andrew Hitchcock, C Neil Hunter, Minkyung Baek, Frank DiMaio, and David Baker · 2023
Cited alongside, same era.
Diffdock-pocket: Diffusion for pocket-level docking with sidechain flexibility
Anonymous · 2023
Cited alongside, same era.
Structure prediction of protein-ligand complexes from sequence information with umol
Patrick Bryant, Atharva Kelkar, Andrea Guljas, Cecilia Clementi, and Frank Noé · 2023
Cited alongside, same era.
A glimpse of the next generation of alphafold
Isomorphic Labs Team and Google DeepMind AlphaFold Team
Cited in the paper.
UMD-fit: Generating realistic ligand conformations for distance-based deep docking models
Eric Alcaide, Ziyao Li, Hang Zheng, Zhifeng Gao, and Guolin Ke · 2023
Later among the works it cites.
Synergistic application of molecular docking and machine learning for improved protein-ligand binding pose prediction
He Yang, Hongrui Lin, Yannan Yuan, Yaqi Li, Rongfeng Zou, Gengmo Zhou, Linfeng Zhang, and Hang Zheng · 2023
Later among the works it cites.
Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences, 2023
Martin Buttenschoen, Garrett M. Morris, and Charlotte M. Deane · 2023
Later among the works it cites.
Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…