Menestrina Luca, Parrondo-Pizarro Raquel, Gómez Ismael, Garcia-Serna Ricard, Boyer Scott, Mestres Jordi
Chemotargets SL, Parc Cientific de Barcelona, Baldiri Reixac 4 (TR-03), 08028 Barcelona, Catalonia, Spain.
Institut de Quimica Computacional i Catalisi, Facultat de Ciencies, Universitat de Girona, Maria Aurelia Capmany 69, 17003 Girona, Catalonia, Spain.
Pharmaceutics. 2025 Feb 28;17(3):308. doi: 10.3390/pharmaceutics17030308.
Modern generative chemistry initiatives aim to produce potent and selective novel synthetically feasible molecules with suitable pharmacokinetic properties. General ranges of physicochemical properties relevant for the absorption, distribution, metabolism, and excretion (ADME) of drugs have been used for decades. However, the therapeutic indication, dosing route, and pharmacodynamic response of the individual drug discovery program may ultimately define a distinct desired property profile. A methodological pipeline to build and validate machine learning (ML) models on physicochemical and ADME properties of small molecules is introduced. The analysis of publicly available data on several ADME properties presented in this work reveals significant differences in the property value distributions across the various levels of the anatomical, therapeutic, and chemical (ATC) drug classification. For most properties, the predicted data distributions agree well with the corresponding distributions derived from experimental data across fourteen drug classes. The refined ADME profiles for ATC drug classes should be useful to guide the generation of advanced lead structures directed toward specific therapeutic indications.
现代生成化学计划旨在生产具有合适药代动力学特性的强效且选择性高的新型合成可行分子。与药物吸收、分布、代谢和排泄（ADME）相关的物理化学性质的一般范围已经使用了数十年。然而，单个药物发现计划的治疗适应症、给药途径和药效学反应最终可能决定一个独特的理想性质概况。本文介绍了一种用于构建和验证基于小分子物理化学性质和ADME性质的机器学习（ML）模型的方法流程。对本研究中呈现的几种ADME性质的公开可用数据的分析揭示了在解剖学、治疗学和化学（ATC）药物分类的各个层面上性质值分布的显著差异。对于大多数性质，预测数据分布与来自14个药物类别的实验数据得出的相应分布非常吻合。ATC药物类别的精细ADME概况应有助于指导针对特定治疗适应症的先进先导结构的生成。
