SAR QSAR Environ Res. 2006 Aug;17(4):371-92. doi: 10.1080/10629360600884462.

Collection and preparation of molecular databases for virtual screening.

SAR and QSAR in environmental research

A K Saxena, P Prathipati

PMID: 16920660 DOI: 10.1080/10629360600884462

Abstract

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.

MeSH terms

• Computational Biology / methods
• Computer Simulation
• Databases, Factual*
• Drug Design*
• Models, Chemical
• Models, Molecular
• Quantitative Structure-Activity Relationship*

Publication Types

• Journal Article