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Cited by 45Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

Characterization of small molecule binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening.

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Ding, Bo, Wang, Jian, Li, Nan, Wang, Wei·2013·Journal of Chemical Information and Modeling
DOI 10.1021/ci300508mPMC PMC3584174
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Abstract

Accurately ranking docking poses remains a great challenge in computer-aided drug design. In this study, we present an integrated approach called MIEC-SVM that combines structure modeling and statistical learning to characterize protein-ligand binding based on the complex structure generated from docking. Using the HIV-1 protease as a model system, we showed that MIEC-SVM can successfully rank the docking poses and consistently outperformed the state-of-art scoring functions when the true positives only account for 1% or 0.5% of all the compounds under consideration. More excitingly, we found that MIEC-SVM can achieve a significant enrichment in virtual screening even when trained on a set of known inhibitors as small as 50, especially when enhanced by a model average approach. Given these features of MIEC-SVM, we believe it provides a powerful tool for searching for and designing new drugs.

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  • Studies evaluating the influence of training size demonstrated that MLR becomes insensitive to the growth of the training size whereas larger training sets can lead to an overall better accuracy of nonlinear scoring functions.

    “Studies evaluating the influence of the training size for the performance of linear and nonlinear scoring functions for affinity prediction demonstrated that MLR becomes insensitive to the growth of the training size whereas larger training sets can lead to an overall better accuracy of nonlinear scoring functions (Ding et al., 2013; Ain et al., 2015; Li et al., 2015a,b; Li H. et al., 2018).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Ding et al., 2013

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Authors
Ding, B., Wang, J., Li, N., & Wang, W.
Affiliations
University of California San Diego (US)
Corresponding author
Wang, Wei
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2013-01-09
Volume
53
Issue
1
Pages
114-122
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci300508m
PMC
PMC3584174
OpenAlex
W2050237550
Cited by
45
References
45
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 5 · 2019: 6 · 2018: 2 · 2017: 5 · 2016: 3 · 2015: 8 · 2014: 1 · 2013: 1
FWCI
3.43
Type
journal article
Language
en
Open access
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of General Medical Sciences (R01GM085188)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
protein-ligand binding, virtual screening, HIV-1 protease, support vector machine, model averaging, small molecule inhibitors, drug design
MeSH terms
Drug Evaluation, Preclinical, Ligands, Protein Conformation, Solvents, Thermodynamics, User-Computer Interface, HIV-1, HIV Protease, HIV Protease Inhibitors, Small Molecule Libraries, Hydrophobic and Hydrophilic Interactions, Support Vector Machine, Molecular Docking Simulation

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