CovalentDock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints
Abstract
Covalent linkage formation is a very important mechanism for many covalent drugs to work. However, partly due to the limitations of proper computational tools for covalent docking, most covalent drugs are not discovered systematically. In this article, we present a new covalent docking package, the CovalentDock, built on the top of the source code of Autodock. We developed an empirical model of free energy change estimation for covalent linkage formation, which is compatible with existing scoring functions used in docking, while handling the molecular geometry constrains of the covalent linkage with special atom types and directional grid maps. Integrated preparation scripts are also written for the automation of the whole covalent docking workflow. The result tested on existing crystal structures with covalent linkage shows that CovalentDock can reproduce the native covalent complexes with significant improved accuracy when compared with the default covalent docking method in Autodock. Experiments also suggest that CovalentDock is capable of covalent virtual screening with satisfactory enrichment performance. In addition, the investigation on the results also shows that the chirality and target selectivity along with the molecular geometry constrains are well preserved by CovalentDock, showing great capability of this method in the application for covalent drug discovery.
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GOLD, Autodock4, CovalentDock, CovDock, DOCKovalent, and DOCK-TITE are examples of docking programs that developed specific methodologies to deal with covalent docking.
“GOLD (Jones et al., 1997), Autodock4 (Bianco et al., 2016), CovalentDock (Ouyang et al., 2013), CovDock (Zhu et al., 2014), DOCKovalent (London et al., 2014), and DOCK-TITE (Scholz et al., 2015) are some examples of docking programs that developed specific methodologies to deal with covalent-docking.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Ouyang et al., 2013
Previously reported results suggest that the AutoDock flexible residue method outperformed GOLD in a dataset of 76 complexes.
“Previously reported results suggest that AutoDock flexible residue method outperformed GOLD in a dataset of 76 complexes.4”
From Covalent docking using autodock: two-point attractor and flexible side chain methods. (2016) · cites this paper as [4]
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- Authors
- Ouyang, X., Zhou, S., Su, C. T. T., Ge, Z., Li, R., & Kwoh, C. K.
- Affiliations
- Nanyang Technological University (SG)
- Peking University (CN)
- Corresponding author
- Li, Runtao
- Kwoh, Chee Keong
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2012-10-04
- Volume
- 34
- Issue
- 4
- Pages
- 326-336
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2042620520
- Cited by
- 134
- References
- 42
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 4 · 2023: 8 · 2022: 8 · 2021: 13 · 2020: 13 · 2019: 8 · 2018: 14 · 2017: 13 · 2016: 14 · 2015: 10 · 2014: 10 · 2013: 4
- FWCI
- 8.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computational Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Nanyang Technological University
- Ministry of Earth Sciences
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- covalent docking, covalent drug discovery, free energy calculation, AutoDock, virtual screening, target selectivity
- MeSH terms
- Animals, Ligands, Protein Binding, Proteins, Software, Thermodynamics, Drug Design, Computer-Aided Design, Molecular Docking Simulation
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