The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automated Docking Screens: A Feasibility Study
Open accessAbstract
Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 A rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 A rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at http://blaster.docking.org .
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Cites
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ZINC 15 – Ligand Discovery for Everyone
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Self-citationDirectory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
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Molecular Docking Screens Using Comparative Models of Proteins
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Self-citationPerceiver CPI: a nested cross-attention network for compound–protein interaction prediction
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DockOpt: A Tool for Automatic Optimization of Docking Models
(2024) · doi:10.1021/acs.jcim.3c01406 · Cited by 7
Metadata
- Authors
- Irwin, J. J., Shoichet, B. K., Mysinger, M. M., Huang, N., Colizzi, F., Wassam, P., & Cao, Y.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-08-31
- Volume
- 52
- Issue
- 18
- Pages
- 5712-5720
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2124074156
- Cited by
- 286
- References
- 4
- FWCI
- 13.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
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