QSAR as a Small-Deformation Theory of Effective Free-Energy Landscapes

Abstract Quantitative structure–activity relationships (QSARs) remain widely used in medicinal chemistry, yet their physical interpretation is often limited and the meaning of their applicability domain (AD) is commonly treated in statistical or descriptor-space terms. Here we propose a conceptual framework in which QSAR is interpreted as a small-deformation theory of effective free-energy landscapes. Ligand binding or chemical substitution is represented as a perturbation of an effective free-energy landscape defined over reduced state variables, and the resulting free-energy response is expressed as a contracted observable over the equilibrium ensemble of the reference system via a Zwanzig-type perturbation formula. At the phenomenological level, this response reduces to a low-order expansion in descriptor space, from which conventional linear QSAR emerges as the leading-order approximation. Within the assumed effective description and under stated regularity conditions, the corresponding first-order coefficients are given exactly by the ensemble-averaged sensitivities of the free-energy landscape to descriptor perturbations, without requiring a cumulant truncation. The AD acquires a physical meaning as the region in which a local low-order expansion of the free-energy response remains valid. In the small-deformation regime, this same region further corresponds to ligand-induced landscape perturbations that remain local, smooth, and sufficiently weak over the reference ensemble. Conversely, in systems for which an effective-landscape description is appropriate, QSAR breakdown may be associated with phenomena such as activity cliffs, induced fit, and state-dependent binding, which can involve qualitative reorganization or rapid reweighting of the effective landscape. For a specified thermodynamic system, the second-cumulant term provides a non-positive variance correction to the leading-order ensemble-average free-energy response, arising from the variance of the landscape perturbation over the reference ensemble. This correction reflects a fluctuation-assisted effect in which configurational heterogeneity within the pre-existing ensemble allows Boltzmann reweighting to redistribute statistical weight toward microstates more strongly influenced by the perturbation. Independently, the exact free-energy response is no greater than the leading-order ensemble-average estimate, consistent with Jensen’s inequality. The present framework is conceptual. Its interpretation of QSAR coefficients as ensemble-averaged sensitivities of the free-energy landscape requires additional assumptions and state-resolved information beyond conventional compound-level QSAR data, and it does not by itself provide a prospective AD method.

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Journal
Journal of Chemical Information and Modeling
Published
2026-09-09
DOI
https://doi.org/10.1021/acs.jcim.6c01230
Primary Topic
Computational Drug Discovery Methods
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article
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article

QSAR as a Small-Deformation Theory of Effective Free-Energy Landscapes

Junichi Okada, Katsuhito Fujiu
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

QSAR as a Small-Deformation Theory of Effective Free-Energy Landscapes

Junichi Okada, Katsuhito Fujiu
article en

Abstract

Abstract Quantitative structure–activity relationships (QSARs) remain widely used in medicinal chemistry, yet their physical interpretation is often limited and the meaning of their applicability domain (AD) is commonly treated in statistical or descriptor-space terms. Here we propose a conceptual framework in which QSAR is interpreted as a small-deformation theory of effective free-energy landscapes. Ligand binding or chemical substitution is represented as a perturbation of an effective free-energy landscape defined over reduced state variables, and the resulting free-energy response is expressed as a contracted observable over the equilibrium ensemble of the reference system via a Zwanzig-type perturbation formula. At the phenomenological level, this response reduces to a low-order expansion in descriptor space, from which conventional linear QSAR emerges as the leading-order approximation. Within the assumed effective description and under stated regularity conditions, the corresponding first-order coefficients are given exactly by the ensemble-averaged sensitivities of the free-energy landscape to descriptor perturbations, without requiring a cumulant truncation. The AD acquires a physical meaning as the region in which a local low-order expansion of the free-energy response remains valid. In the small-deformation regime, this same region further corresponds to ligand-induced landscape perturbations that remain local, smooth, and sufficiently weak over the reference ensemble. Conversely, in systems for which an effective-landscape description is appropriate, QSAR breakdown may be associated with phenomena such as activity cliffs, induced fit, and state-dependent binding, which can involve qualitative reorganization or rapid reweighting of the effective landscape. For a specified thermodynamic system, the second-cumulant term provides a non-positive variance correction to the leading-order ensemble-average free-energy response, arising from the variance of the landscape perturbation over the reference ensemble. This correction reflects a fluctuation-assisted effect in which configurational heterogeneity within the pre-existing ensemble allows Boltzmann reweighting to redistribute statistical weight toward microstates more strongly influenced by the perturbation. Independently, the exact free-energy response is no greater than the leading-order ensemble-average estimate, consistent with Jensen’s inequality. The present framework is conceptual. Its interpretation of QSAR coefficients as ensemble-averaged sensitivities of the free-energy landscape requires additional assumptions and state-resolved information beyond conventional compound-level QSAR data, and it does not by itself provide a prospective AD method.

Journal of Chemical Information and Modeling
Bunkyo University (JP), The University of Tokyo (JP)
Affordable and clean energy
Openalex Percentile: Top 8%
Computational Drug Discovery Methods
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