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Cited by 357Computational Drug Discovery MethodsMachine Learning in Materials ScienceAdvanced Chemical Physics Studies

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

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Ain, Qurrat Ul, Aleksandrova, Antoniya, Roessler, Florian D., Ballester, Pedro J.·2015·WIREs Computational Molecular Science
DOI 10.1002/wcms.1225PMC PMC4832270
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Abstract

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure‐based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine‐learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine‐learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert‐selected structural features can be strongly improved by a machine‐learning approach based on nonlinear regression allied with comprehensive data‐driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405–424. doi: 10.1002/wcms.1225 This article is categorized under: Computer and Information Science > Chemoinformatics

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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 Ain et al., 2015

  • It is possible to use scoring functions trained with regression methods to classify active and inactive molecules given a predetermined range of affinity data for defining active and inactive compounds.

    “It is possible to use scoring functions trained with regression methods to classify active and inactive molecules given a predetermined range of affinity data for defining active and inactive compounds (Ain et al., 2015).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Ain et al., 2015

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Research works printed but not linked: 1

  1. [83]Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE. Correction to ‘a machine learning‐based method to improve docking scoring functions and its application to drug repurposing’. J Chem Inf Model 2011, 51: 408– 419. 21291174 PMC3076728

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Authors
Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J.
Affiliations
  • University of Cambridge (GB)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Ballester, Pedro J.
ORCID
Venue
WIREs Computational Molecular Science
Publisher
Wiley
Published
2015-08-28
Volume
5
Issue
6
Pages
405-424
ISSN
  • 1759-0876 (ISSN-L)
  • 1759-0884
DOI
10.1002/wcms.1225
PMC
PMC4832270
OpenAlex
W1950993160
Cited by
357
References
110
Citations by year
2026: 18 · 2025: 22 · 2024: 43 · 2023: 50 · 2022: 48 · 2021: 60 · 2020: 32 · 2019: 39 · 2018: 18 · 2017: 17 · 2016: 10
FWCI
1.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Wiley Interdisciplinary Reviews Computational Molecular Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
Funders
  • French Government «Investissements d'Avenir» program (n° ANR-11-IDEX-0001-02)
  • UK Medical Research Council Methodology Research Fellowship (G0902106)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
scoring functions, machine learning, structure-based drug design, binding affinity prediction, virtual screening, molecular docking, nonlinear regression, feature selection, synthetic data generation, training data size

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