用简单图论特征预测并设计目标水溶性分子，速度快且精准。
A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility
- 基于图论特征和线性回归建模，实现高效预测。
- 在29个数据集上准确率达0.7191~0.9377。
- 可数学推导出最优结构分子，适用于药物研发。
水溶性（AS）是药物发现与材料设计中的关键理化性质。本文提出一种统一方法，基于简单的确定性图论描述符、多元线性回归（MLR）与混合整数线性规划（MILP），预测并推断具有目标水溶性的化学分子。通过前向逐步选择的描述符，使最简的MLR模型在29个多样化数据集上达到0.7191~0.9377的预测准确率，显著优于现有方法。将描述符与学习模型转化为MILP后，可在6~1204秒内推导出满足目标水溶性、结构约束且含最多50个非氢原子的数学最优分子。结果表明，简单图论特征与分子水溶性存在强相关性，无需复杂化学描述符或计算成本高的机器学习模型即可实现高效推理。代码已开源：https://github.com/ku-dml/mol-infer/tree/master/AqSol。
原文摘要 · Abstract (English)
Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors, multiple linear regression (MLR) and mixed integer linear programming (MILP). Selected descriptors based on a forward stepwise procedure enabled the simplest regression model, MLR, to achieve significantly good prediction accuracy compared to the existing approaches, achieving the accuracy in the range [0.7191, 0.9377] for 29 diverse datasets. By simulating these descriptors and learning models as MILPs, we inferred mathematically exact and optimal compounds with the desired AS, prescribed structures, and up to 50 non-hydrogen atoms in a reasonable time range [6, 1204] seconds. These findings indicate a strong correlation between the simple graph-theoretic descriptors and the AS of compounds, potentially leading to a deeper understanding of their AS without relying on widely used complicated chemical descriptors and complex machine learning models that are computationally expensive, and therefore difficult to use for inference. An implementation of the proposed approach is available at https://github.com/ku-dml/mol-infer/tree/master/AqSol.
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