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），以分子片段计数作为描述符，构建了针对人类绝对口服生物利用度、血浆蛋白结合率与尿排泄率的定量构效关系（quantitative structure−property relationship, QSPR）模型集。针对单种药代动力学性质，由20或30个模型组成的模型集的共识评分，可显著提升相关系数并降低标准误差。本研究同时鉴定出了能够增强或削弱该类药代动力学性质的关键分子片段。针对口服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分析。在MDDR（MDL药物数据报告，一款收录已进入或即将进入研发阶段的药物及药物先导物的数据库）中，可提升口服生物利用度的片段的命中比率，显著高于削弱口服生物利用度的片段。而在ACD（可获得化学品目录，一款收录各类可获得化合物的数据库）中，则观测到了相反的趋势。