Enhancing Drug Discovery Through In Silico Screening: Strategies to Increase True Positives Retrieval Rates

J.      Kirchmair; S.      Distinto; D.      Schuster; G.      Spitzer; T.      Langer; G.      Wolber

Abstract

Computational chemistry software for lead discovery has become well established in pharmaceutical industry and has found its way to the desktop computers of medicinal chemists for different purposes, providing insight on the mode of action and binding properties, and creating new ideas for lead structure refinement. In this review we investigate the performance and reliability of recent state-of-the-art data modeling techniques, as well as ligand-based and structurebased modeling approaches for 3D virtual screening. We discuss and summarize recently published success stories and lately developed techniques. Parallel screening is one of these emerging approaches allowing for efficient activity in silico profiling of several compounds against different targets or anti-targets simultaneously. This is of special interest to medicinal chemists, as the approach allows revealing unknown binding modes (‘target-fishing’) as well as integrated ADME profiling or – more generally – the prediction of off-target effects.

Keywords: Data modeling, virtual screening, parallel screening, ADME/Tox profiling, activity profiling, target fishing, pharmacophore modeling, protein-ligand docking