Cruciani Gabriele, Pastor Manuel, Mannhold Raimund
Dipartimento di Chimica, Laboratorio di Chemiometria, Universita di Perugia, Via Elce di Sotto 10, 1-06123 Perugia, Italy.
J Med Chem. 2002 Jun 20;45(13):2685-94. doi: 10.1021/jm0011326.
Database mining methods rely on the molecular descriptors used to characterize a structural database. In the present investigation, five different types of descriptors (log P, UNITY fingerprints, ISIS keys, VolSurf, and GRIND) are applied to characterize various databases (n = 1007, 100, and 229) comprising drugs almost exclusively. The validity of the descriptors is comparatively analyzed via principal component analysis and its hierarchical variant, consensus principal component analysis. Both pharmacodynamic and pharmacokinetic aspects of database mining are treated. For pharmacodynamic aspects, clustering behavior achieved with the different descriptors is tested on the chemically homogeneous beta-blockers, benzodiazepines, and penicillins and on the chemically more diverse class I antiarrhythmics. The following ranking is observed: UNITY fingerprints > ISIS keys and GRIND > VolSurf > log P. Regarding information content, the CPCA superweight plot indicates similarity between fingerprints and ISIS keys as well as between VolSurf and log P, while GRIND differs from all the remaining descriptors. Solubility data and blood/brain barrier penetrating behavior serve as test cases for pharmacokinetic aspects. Comparison of the descriptors applied to these data reveals that VolSurf has the most realistic and consistent behavior, GRIND shows intermediate behavior, while UNITY fingerprints and ISIS keys are not well suited for pharmacokinetic profiling. From this comparative analysis, we conclude that VolSurf descriptors exhibit particular advantages in treating pharmacokinetic aspects; UNITY fingerprints, ISIS keys, and GRIND descriptors are of special value for tackling pharmacodynamic aspects of database mining. The parameter log P is of limited applicability in database mining because of rather poor reliability and lack of completeness of data.
数据库挖掘方法依赖于用于表征结构数据库的分子描述符。在本研究中，应用了五种不同类型的描述符（log P、UNITY指纹、ISIS键、VolSurf和GRIND）来表征几乎完全由药物组成的各种数据库（n = 1007、100和229）。通过主成分分析及其层次变体——共识主成分分析对描述符的有效性进行了比较分析。同时探讨了数据库挖掘的药效学和药代动力学方面。对于药效学方面，在化学性质均一的β受体阻滞剂、苯二氮䓬类药物和青霉素以及化学性质更为多样的I类抗心律失常药物上测试了用不同描述符实现的聚类行为。观察到以下排名：UNITY指纹>ISIS键和GRIND>VolSurf>log P。关于信息含量，CPCA超重图表明指纹和ISIS键之间以及VolSurf和log P之间存在相似性，而GRIND与所有其余描述符不同。溶解度数据和血脑屏障穿透行为用作药代动力学方面的测试案例。对应用于这些数据的描述符进行比较后发现，VolSurf具有最现实和一致的行为，GRIND表现出中等行为，而UNITY指纹和ISIS键不太适合药代动力学分析。从这一比较分析中，我们得出结论，VolSurf描述符在处理药代动力学方面表现出特殊优势；UNITY指纹、ISIS键和GRIND描述符在解决数据库挖掘的药效学方面具有特殊价值。参数log P在数据库挖掘中的适用性有限，因为其可靠性相当差且数据缺乏完整性。
