Literature DB >> 19754393

Docking screens: right for the right reasons?

Peter Kolb1, John J Irwin.   

Abstract

Whereas docking screens have emerged as the most practical way to use protein structure for ligand discovery, an inconsistent track record raises questions about how well docking actually works. In its favor, a growing number of publications report the successful discovery of new ligands, often supported by experimental affinity data and controls for artifacts. Few reports, however, actually test the underlying structural hypotheses that docking makes. To be successful and not just lucky, prospective docking must not only rank a true ligand among the top scoring compounds, it must also correctly orient the ligand so the score it receives is biophysically sound. If the correct binding pose is not predicted, a skeptic might well infer that the discovery was serendipitous. Surveying over 15 years of the docking literature, we were surprised to discover how rarely sufficient evidence is presented to establish whether docking actually worked for the right reasons. The paucity of experimental tests of theoretically predicted poses undermines confidence in a technique that has otherwise become widely accepted. Of course, solving a crystal structure is not always possible, and even when it is, it can be a lot of work, and is not readily accessible to all groups. Even when a structure can be determined, investigators may prefer to gloss over an erroneous structural prediction to better focus on their discovery. Still, the absence of a direct test of theory by experiment is a loss for method developers seeking to understand and improve docking methods. We hope this review will motivate investigators to solve structures and compare them with their predictions whenever possible, to advance the field.

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Year:  2009        PMID: 19754393      PMCID: PMC3383315          DOI: 10.2174/156802609789207091

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  75 in total

1.  Quantifying the relationships among drug classes.

Authors:  Jérôme Hert; Michael J Keiser; John J Irwin; Tudor I Oprea; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2008-03-13       Impact factor: 4.956

2.  Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase.

Authors:  Kerim Babaoglu; Anton Simeonov; John J Irwin; Michael E Nelson; Brian Feng; Craig J Thomas; Laura Cancian; M Paola Costi; David A Maltby; Ajit Jadhav; James Inglese; Christopher P Austin; Brian K Shoichet
Journal:  J Med Chem       Date:  2008-03-12       Impact factor: 7.446

3.  Rescoring docking hit lists for model cavity sites: predictions and experimental testing.

Authors:  Alan P Graves; Devleena M Shivakumar; Sarah E Boyce; Matthew P Jacobson; David A Case; Brian K Shoichet
Journal:  J Mol Biol       Date:  2008-01-30       Impact factor: 5.469

4.  Docking study yields four novel inhibitors of the protooncogene Pim-1 kinase.

Authors:  Albert C Pierce; Marc Jacobs; Cameron Stuver-Moody
Journal:  J Med Chem       Date:  2008-02-22       Impact factor: 7.446

5.  A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists.

Authors:  Stanislav Engel; Amanda P Skoumbourdis; John Childress; Susanne Neumann; Jeffrey R Deschamps; Craig J Thomas; Anny-Odile Colson; Stefano Costanzi; Marvin C Gershengorn
Journal:  J Am Chem Soc       Date:  2008-03-22       Impact factor: 15.419

6.  Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors.

Authors:  Matthieu Montes; Emmanuelle Braud; Maria A Miteva; Mary-Lorène Goddard; Odile Mondésert; Stéphanie Kolb; Marie-Priscille Brun; Bernard Ducommun; Christiane Garbay; Bruno O Villoutreix
Journal:  J Chem Inf Model       Date:  2007-12-22       Impact factor: 4.956

Review 7.  Fragment-based drug discovery using rational design.

Authors:  H Jhoti
Journal:  Ernst Schering Found Symp Proc       Date:  2007

8.  Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening.

Authors:  Irina G Tikhonova; Chi Shing Sum; Susanne Neumann; Stanislav Engel; Bruce M Raaka; Stefano Costanzi; Marvin C Gershengorn
Journal:  J Med Chem       Date:  2008-01-15       Impact factor: 7.446

9.  Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening.

Authors:  Claudio N Cavasotto; Andrew J W Orry; Nicholas J Murgolo; Michael F Czarniecki; Sue Ann Kocsi; Brian E Hawes; Kim A O'Neill; Heather Hine; Marybeth S Burton; Johannes H Voigt; Ruben A Abagyan; Marvin L Bayne; Frederick J Monsma
Journal:  J Med Chem       Date:  2008-01-17       Impact factor: 7.446

10.  Novel PPAR-gamma agonists identified from a natural product library: a virtual screening, induced-fit docking and biological assay study.

Authors:  Noeris K Salam; Tom H-W Huang; Bhavani P Kota; Moon S Kim; Yuhao Li; David E Hibbs
Journal:  Chem Biol Drug Des       Date:  2007-12-18       Impact factor: 2.817

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  30 in total

1.  Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening.

Authors:  Olli T Pentikäinen; Pekka A Postila
Journal:  Methods Mol Biol       Date:  2021

Review 2.  Receptor-ligand molecular docking.

Authors:  Isabella A Guedes; Camila S de Magalhães; Laurent E Dardenne
Journal:  Biophys Rev       Date:  2013-12-21

3.  Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores.

Authors:  Mark L Benson; John C Faver; Melek N Ucisik; Danial S Dashti; Zheng Zheng; Kenneth M Merz
Journal:  J Comput Aided Mol Des       Date:  2012-04-04       Impact factor: 3.686

4.  Ligand Identification Scoring Algorithm (LISA).

Authors:  Zheng Zheng; Kenneth M Merz
Journal:  J Chem Inf Model       Date:  2011-05-25       Impact factor: 4.956

5.  Assessing and improving the performance of consensus docking strategies using the DockBox package.

Authors:  Jordane Preto; Francesco Gentile
Journal:  J Comput Aided Mol Des       Date:  2019-10-01       Impact factor: 3.686

6.  The Fundamental Role of Flexibility on the Strength of Molecular Binding.

Authors:  Christopher Forrey; Jack F Douglas; Michael K Gilson
Journal:  Soft Matter       Date:  2012-05-14       Impact factor: 3.679

Review 7.  Challenges and opportunities for new protein crystallization strategies in structure-based drug design.

Authors:  Jessica Lynn Grey; David H Thompson
Journal:  Expert Opin Drug Discov       Date:  2010-11       Impact factor: 6.098

8.  Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Authors:  Spencer S Ericksen; Haozhen Wu; Huikun Zhang; Lauren A Michael; Michael A Newton; F Michael Hoffmann; Scott A Wildman
Journal:  J Chem Inf Model       Date:  2017-07-12       Impact factor: 4.956

9.  Virtual Screening with AutoDock: Theory and Practice.

Authors:  Sandro Cosconati; Stefano Forli; Alex L Perryman; Rodney Harris; David S Goodsell; Arthur J Olson
Journal:  Expert Opin Drug Discov       Date:  2010-06-01       Impact factor: 6.098

10.  Protein-Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation.

Authors:  Saeed Izadi; Boris Aguilar; Alexey V Onufriev
Journal:  J Chem Theory Comput       Date:  2015-08-21       Impact factor: 6.006

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