A Probabilistic Approach to High Throughput Drug Discovery

Authors: Labute P.; Nilar S.; Williams C.

Source: Combinatorial Chemistry & High Throughput Screening, Volume 5, Number 2, March 2002 , pp. 135-145(11)

Publisher: Bentham Science Publishers

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

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.

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

Language: English

Document Type: Review article

DOI: 10.2174/1386207024607329

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$55.10 plus tax Refund Policy