Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913717712584704 |
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| author | Zhu, Jianshen Takekida, Mao Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya |
| author_facet | Zhu, Jianshen Takekida, Mao Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming Zhu, Jianshen Takekida, Mao Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya Chemical Physics Machine Learning 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. |
| title | Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2503.01849 |