Computational Pharmaceutical Chemistry – Novel Technologies for Lead Optimization and the Prediction of ADMET Properties

Abstract

The prediction of affinities of ligands binding to a target protein represents a major challenge in modern computer-aided drug design. To contribute towards this goal, we have developed a new technology to identify feasible binding modes of protein-bound, biomedically interesting molecules and to compute their binding affinity using multidimensional quantitative structure-activity relationships (QSAR). In our approach, the flexibility of the protein is explicitly simulated. Applications of the underlying technology to G protein-coupled receptors, nuclear receptors and cytochrome P450 show the ability of this approach to predict the binding affinity of diverse sets of ligands to a common protein, and suggest its potential to predict adverse or toxic effects of drugs and chemicals in silico.