The DOI matches its Crossref record; 194 of the 194 references it cites were found in a registry or an index.
Key Topics in Molecular Docking for Drug Design
Open accessAbstract
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
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Karki, N., Verma, N., Trozzi, F., Tao, P., Kraka, E., & Zoltowski, B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041573 · Cited by 43
A new paradigm for applying deep learning to protein–ligand interaction prediction
Wang, Z., Wang, S., Li, Y., Guo, J., Wei, Y., Mu, Y., Zheng, L., & Li, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae145 · Cited by 41
Metadata
- Authors
- Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva-Jr, F. P.
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2019-09-15
- Volume
- 20
- Issue
- 18
- Pages
- 4574
- ISSN
- 1422-0067 (ISSN-L)
- OpenAlex
- W2972485984
- Cited by
- 641
- References
- 194
- FWCI
- 23.74
- Type
- journal article
- Language
- en
- Open access
- gold
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