title: QSAR models for predicting enzymatic hydrolysis of new chemical entities in 'soft-drug' design.
abstract: The work described here is aimed at developing QSAR models capable of predicting in vitro human plasma lability/stability. They were built based on a dataset comprising about 200 known compounds. 3D structures of the molecules were drawn, optimized and submitted to the calculation of molecular descriptors that enabled selecting different TR/TS set pairs, subsequently exploited to develop QSAR models. Several 'machine learning' algorithms were explored in order to obtain suitable classification models, which were then validated on the relevant TS sets. Moreover the predictive ability of the best performing models was assessed on a Prediction set (PS) comprising about 40 molecules, not strictly related, from a structural point of view, to the initial dataset, but (obviously) comprised within the validity domain of the QSAR models obtained. The study allowed selecting predictive models enabling the classification of New Chemical Entities with regard to hydrolysis rate, that may be exploited for soft-drug design.
authors.affiliation: Department of Chemistry and Industrial Chemistry, University of Pisa, Via Risorgimento 35, 56126 Pisa, Italy.
authors.forename: I
authors.initials: I
authors.lastname: Massarelli
authors.forename: M
authors.initials: M
authors.lastname: Macchia
authors.forename: F
authors.initials: F
authors.lastname: Minutolo
authors.forename: G
authors.initials: G
authors.lastname: Prota
authors.forename: A M
authors.initials: AM
authors.lastname: Bianucci
mesh_terms: D000465:Algorithms; D020650:Combinatorial Chemistry Techniques; D015195:Drug Design; D006801:Humans; D006868:Hydrolysis; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000097:blood*; D021281:Quantitative Structure-Activity Relationship; D013237:Stereoisomerism
chemical_list: D004364:Pharmaceutical Preparations