A Probabilistic Approach to High Throughput Drug Discovery

Author(s): Paul Labute, Shahul Nilar, Christopher Williams.

Journal Name: Combinatorial Chemistry & High Throughput Screening

Volume 5 , Issue 2 , 2002

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A methodology is presented in which high throughput screening experimental data are used to construct a probabilistic QSAR model which is subsequently used to select building blocks for a virtual combinatorial library. The methodology is based upon statistical probability estimation and not regression. The methodology is applied to the construction of two focused virtual combinatorial libraries: one for cyclic GMP phosphodiesterase type V inhibitors and one for acyl-CoA:cholesterol O-acyltransferase inhibitors. The results suggest that the methodology is capable of selecting combinatorial substituents that lead to active compounds starting with binary (pass / fail) activity measurements.

Keywords: high throughput drug discovery, acat, cholesterol O-acyltransferase(acat)

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Article Details

Year: 2002
Page: [135 - 145]
Pages: 11
DOI: 10.2174/1386207024607329
Price: $58

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