Genetic Algorithm-Optimized QSPR Models for Bioavailability, Protein Binding, and Urinary Excretion

In this work, a genetic algorithm (GA) was applied to build up a set of QSPR (quantitative structure−property relationship) models for human absolute oral bioavailability, plasma protein binding, and urinary
excretion using the counts of molecular fragments as descriptors. For a pharmacokinetic property, the
consensus score of a set of models (20 or 30) was found to improve the correlation coefficient and reduce
the standard error significantly. Key fragments that may boost or reduce pharmacokinetic properties were
also identified. Databases searches were performed for a set of key fragments identified by bioavailability
models. The percentage of hit rates of bioavailability-boosting fragments were significantly higher than
those of bioavailability-reducing fragments for MDDR (MDL Drug Data Report), a database of drugs and
drug leads entered or entering development. On the other hand, the opposite trend was observed for ACD
(Available Chemicals Directory), a database of all kinds of available compounds.

本研究采用遗传算法(GA)构建了一系列定量构效关系(QSPR, quantitative structure−property relationship)模型，以分子片段计数作为描述符，针对人类绝对口服生物利用度、血浆蛋白结合率及尿排泄率三类药代动力学性质开展建模。针对单类药代动力学性质，集成20或30个模型的共识评分可显著提升相关系数，同时大幅降低标准误差。本研究同时鉴定出了可增强或削弱药代动力学性质的关键分子片段。针对生物利用度模型所鉴定出的关键分子片段，研究团队开展了数据库检索工作。在MDDR（MDL Drug Data Report，即收录已上市或处于研发阶段的药物及药物先导物的数据库）中，可提升生物利用度的片段的命中百分比显著高于降低生物利用度的片段。而在ACD（Available Chemicals Directory，即收录各类可获取化合物的数据库）中，则呈现出相反的趋势。