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Cited by 718Computational Drug Discovery MethodsCholinesterase and Neurodegenerative Diseasesvaccines and immunoinformatics approaches

A Review on Applications of Computational Methods in Drug Screening and Design

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Lin, Xiaoqian, Li, Xiu, Lin, Xubo·2020·Molecules
DOI 10.3390/molecules25061375PMC PMC7144386
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Abstract

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

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  • Various computational models for target prediction have been developed, leading to notable improvements in predictive accuracy and efficiency.

    “To this end, various computational models for target prediction have been developed, leading to notable improvements in predictive accuracy and efficiency38–40.”

    From Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha (2025) · cites this paper as [40]

    Section 5.1 identifies random forests as a method used for drug target prediction, supporting the development of computational approaches for that task. The Conclusions say machine learning may improve efficiency and precision in drug screening and design generally, but do not establish that target-prediction models have already produced notable improvements in both accuracy and efficiency. Page numbers were not available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Lin, X., Li, X., & Lin, X.
Affiliations
  • Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China
  • Beihang University (CN)
Corresponding author
Li, Xiu
ORCID
Venue
Molecules
Publisher
MDPI AG
Published
2020-03-18
Volume
25
Issue
6
Pages
1375
ISSN
1420-3049 (ISSN-L)
DOI
10.3390/molecules25061375
PMC
PMC7144386
OpenAlex
W3010761615
Cited by
718
References
5
Citations by year
2026: 79 · 2025: 121 · 2024: 157 · 2023: 137 · 2022: 106 · 2021: 86 · 2020: 27 · 2019: 3
FWCI
7.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecules (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (21903002)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
drug discovery, drug screening, molecular docking, pharmacophore modeling, QSAR, ligand-based drug design, structure-based drug design, de novo drug design, machine learning in drug discovery, drug action mechanisms
MeSH terms
Machine Learning, Drug Design, Drug Discovery, Molecular Dynamics Simulation, Molecular Docking Simulation

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