2024
DOI: 10.1021/acsomega.3c09047
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Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships

Raku Shirasawa,
Katsushi Takaki,
Tomoyuki Miyao

Abstract: In the pursuit of optimal quantitative structure− activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed … Show more

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