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Cited by 621

The DOI matches its Crossref record; 233 of the 233 references it cites were found in a registry or an index.

Use of molecular docking computational tools in drug discovery

·2021·Progress in Medicinal Chemistry

Abstract

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

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Metadata

Venue
Progress in Medicinal Chemistry
Publisher
Elsevier
Published
2021
Volume
60
Pages
273-343
ISSN
0079-6468 (ISSN-L)
OpenAlex
W3172747904
Cited by
621
References
233
FWCI
199.94
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Use of molecular docking computational tools in drug discovery. https://app.cochat.ai/papers/10.1016/bs.pmch.2021.01.004
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Use of molecular docking computational tools in drug discovery. (2021). Progress in Medicinal Chemistry. https://doi.org/10.1016/bs.pmch.2021.01.004

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.