Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows

Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine

Ajay N. Jain · 2003

Earlier work this paper cites.

Glide: A new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy

R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis, and P. S. Shenkin · 2004

Earlier work this paper cites.

Glide: A new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening

Thomas A Halgren, Robert B Murphy, Richard A Friesner, Hege S Beard, Leah L Frye, W Thomas Pollard, and Jay L Banks · 2004

Earlier work this paper cites.

Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search

Ajay N. Jain · 2007

Earlier work this paper cites.

Effects of protein conformation in docking: Improved pose prediction through protein pocket adaptation

Ajay N Jain · 2009

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.

Surflex-Dock: Docking benchmarks and real-world application

Russell Spitzer and Ajay N Jain · 2012

Earlier work this paper cites.

Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise

David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho · 2013

Cited alongside, same era.

Knowledge-guided docking: Accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock

Ann E Cleves and Ajay N Jain · 2015

Cited alongside, same era.

ForceGen 3D structure and conformer generation: From small lead-like molecules to macrocyclic drugs

Ann E Cleves and Ajay N Jain · 2017

Cited alongside, same era.

ANI-1: An extensible neural network potential with dft accuracy at force field computational cost

Justin S Smith, Olexandr Isayev, and Adrian E Roitberg · 2017

Cited alongside, same era.

Complex macrocycle exploration: Parallel, heuristic, and constraint-based conformer generation using forcegen

Ajay N Jain, Ann E Cleves, Qi Gao, Xiao Wang, Yizhou Liu, Edward C Sherer, and Mikhail Y Reibarkh · 2019

Cited alongside, same era.

Structure-and ligand-based virtual screening on DUD-E + : Performance dependence on approximations to the binding pocket

Ann E Cleves and Ajay N Jain · 2020

Later among the works it cites.

Autodock vina 1.2. 0: New docking methods, expanded force field, and Python bindings

Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021

Later among the works it cites.

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

Later among the works it cites.

Diffdock: Diffusion steps, twists, and turns for molecular docking

Original

Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2022

Later among the works it cites.

Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences

Original

Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane · 2023