Traditional QSAR/QSPR and inverse QSAR/QSPR methods often assume that chemical properties are dictated by single molecules, overlooking the influence of molecular interactions and environmental factors. In this paper, we introduce a novel QSAR/QSPR framework that can capture the combined effects of multiple molecules (e.g., small molecules or polymers) and experimental conditions on property values. We design a feature function to integrate the information of multiple molecules and the environment. Specifically, for the property Flory-Huggins χ-parameter, which characterizes the thermodynamic properties between the solute and the solvent, and varies in temperatures, we demonstrate through computational experimental results that our approach can achieve a competitively high learning performance compared to existing works on predicting χ-parameter values, while inferring the solute polymers with up to 50 non-hydrogen atoms in their monomer forms in a relatively short time. A comparison study with the simulation software J-OCTA demonstrates that the polymers inferred by our methods are of high quality.
@article{arxiv.2503.01849,
title = {Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming},
author = {Jianshen Zhu and Mao Takekida and Naveed Ahmed Azam and Kazuya Haraguchi and Liang Zhao and Tatsuya Akutsu},
journal= {arXiv preprint arXiv:2503.01849},
year = {2025}
}