Cited by 236Computational Drug Discovery MethodsExplainable Artificial Intelligence (XAI)Machine Learning in Materials Science

Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future

Polishchuk, Pavel·2017·Journal of Chemical Information and Modeling

Abstract

This paper is an overview of the most significant and impactful interpretation approaches of quantitative structure-activity relationship (QSAR) models, their development, and application. The evolution of the interpretation paradigm from "model → descriptors → (structure)" to "model → structure" is indicated. The latter makes all models interpretable regardless of machine learning methods or descriptors used for modeling. This opens wide prospects for application of corresponding interpretation approaches to retrieve structure-property relationships captured by any models. Issues of separate approaches are discussed as well as general issues and prospects of QSAR model interpretation.

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Crossref · 10.1021/acs.jcim.7b00274

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  • For some PFAS, the only available values for physical and chemical properties are estimates derived from mathematical models such as QSAR models.

    “In fact, for some PFAS, the only available values for physical and chemical properties are estimates derived from mathematical models, such as quantitative structure–activity relationship (QSAR) models [18], which can approximate the chemical and physical properties of compounds based on their chemical structure, rather values from direct measurements [19,20].”

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Authors
Polishchuk, P.
Affiliations
Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská 1333/5, 779 00 Olomouc, Czech Republic
Venue
Journal of Chemical Information and Modeling
Published
2017-10-13
Volume
57
Issue
11
Pages
2618-2639
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
OpenAlex
W2760085125
Cited by
236
References
116
Citations by year
2026: 23 · 2025: 22 · 2024: 30 · 2023: 26 · 2022: 39 · 2021: 33 · 2020: 23 · 2019: 25 · 2018: 14 · 2017: 1
FWCI
1.74
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
Funders
Russian Science Foundation (14-43-00024)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
structure-activity relationship, model interpretability, structure-property relationships, machine learning models, molecular descriptors
MeSH terms
Machine Learning, Models, Theoretical, Quantitative Structure-Activity Relationship

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