Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming
This addresses the limitation of traditional QSAR/QSPR methods that ignore molecular interactions and environmental effects, offering a more accurate approach for computational chemistry applications.
The paper tackles the problem of predicting molecular properties by accounting for multiple molecules and environmental factors, achieving competitive learning performance for the Flory-Huggins χ-parameter and inferring solute polymers with up to 50 non-hydrogen atoms quickly.
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.