کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1360542 981439 2009 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
QSAR models for predicting enzymatic hydrolysis of new chemical entities in ‘soft-drug’ design
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آلی
پیش نمایش صفحه اول مقاله
QSAR models for predicting enzymatic hydrolysis of new chemical entities in ‘soft-drug’ design
چکیده انگلیسی

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.

Compounds showing different enzymatic hydrolysis rates: very fast (left) and very slow (right).Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Bioorganic & Medicinal Chemistry - Volume 17, Issue 10, 15 May 2009, Pages 3543–3556
نویسندگان
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