考虑分子与环境互动，用混合整数规划预测化学性质。
Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming
- 构建多分子+环境特征函数，融合交互影响。
- 可快速推断含50个非氢原子的聚合物，预测精度高。
- 适合需考虑复杂环境的分子设计与材料发现场景。
传统QSAR/QSPR及逆向方法常假设化学性质仅由单个分子决定，忽略分子间相互作用与环境因素的影响。本文提出一种新框架，能捕捉多个分子（如小分子或聚合物）及实验条件对性质的综合影响。设计特征函数以整合多分子与环境信息。针对表征溶质-溶剂热力学特性的Flory-Huggins χ参数（随温度变化），计算实验表明，本方法在预测χ参数值上表现优异，相比现有工作具有竞争力；同时可在较短时间内推断出单体含最多50个非氢原子的溶质聚合物。与仿真软件J-OCTA对比显示，所推断聚合物质量优良。
原文摘要 · Abstract (English)
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.
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