2D QSAR Consensus Prediction for High-Throughput Virtual Screening. An Application to COX-2 Inhibition Modeling and Screening of the NCI Database

Nicolas Baurin,† Jean-Christophe Mozziconacci,† Eric Arnoult,† Philippe Chavatte,‡ Christophe Marot,† and Luc Morin-Allory*†
Institut de Chimie Organique et Analytique, UMR 6005, Universit d'Orlans, BP 6759, F-45067 Orlans Cedex 2, France, and Institut de Chimie Pharmaceutique Albert Lespagnol, Universit de Lille 2, BP 83, F-59006 Lille Cedex, France
J. Chem. Inf. Comput. Sci., 2004, 44 (1), pp 276–285
DOI: 10.1021/ci0341565
Publication Date (Web): October 23, 2003
Copyright © 2004 American Chemical Society
†

 Université d'Orléans.

,
‡

 Université de Lille 2.

,
*

 Corresponding author phone:  +33 (0) 2 38 41 70 42; e-mail:  luc.morin-allory@univ-orleans.fr.

Abstract

Using classification (SOM, LVQ, Binary, Decision Tree) and regression algorithms (PLS, BRANN, k-NN, Linear), this paper details the building of eight 2D-QSAR models from a 266 COX-2 inhibitor training set. The predictive performances of these eight models were subsequently compared using an 88 COX-2 inhibitor test set. Each ligand is described by 52 2D descriptors expressed as van der Waals Surface Areas (P_VSA) and its COX-2 binding IC50. One of our best predictive models is the neural network model (BRANN), which is able to select a subset, from the 88 ligand test set, that contains 94% COX-2 active inhibitors (pIC50 > 7.5) and detects 71% of all the actives. We then introduce a QSAR consensus prediction protocol that is shown to be more predictive than any single QSAR model:  our C3 consensus approach is able to select a subset from the 88 ligand test set that contains 94% active inhibitors and 83% of all the actives. The 2D QSAR consensus protocol was finally applied to the high-throughput virtual screening of the NCI database, containing 193 477 organic compounds.

View: Full Text HTML | Hi-Res PDF

Tools

History

  • Published In Issue January 26, 2004
  • Received July 25, 2003

Recommend & Share

Related Content

Other ACS content by these authors: