Toward Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming.

ABSTRACT: Traditional QSAR/QSPR and inverse QSAR/QSPR methods often assume that chemical properties are dictated by single molecules, overlooking the influence of multibody molecular interactions and environmental factors. In this paper, we introduce a novel inverse QSAR/QSPR framework that can capture the combined effects of multiple interacting molecules (e.g., small molecules or polymers) and experimental conditions on property values. To the best of our knowledge, this is the first machine learning-based inverse QSAR/QSPR framework to explicitly integrate multiple interacting molecules and environmental factors. We design a feature function to integrate the information on multiple interacting molecules and the environment. Specifically, for the property Flory-Huggins χ-parameter, which character

SUBMITTER: Zhu J 

PROVIDER: S-EPMC12529158 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature