A Probabilistic Approach to High Throughput Drug Discovery

Title: A Probabilistic Approach to High Throughput Drug Discovery

Volume: 5 Issue: 2

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Labute Paul, Nilar Shahul and Williams Christopher, A Probabilistic Approach to High Throughput Drug Discovery, Combinatorial Chemistry & High Throughput Screening 2002; 5 (2) . https://dx.doi.org/10.2174/1386207024607329