Docking, scoring and binding-affinity prediction in computer-aided drug discovery : I development of a scoring function for quantifying binding affinities

Docking and scoring are widely used in nowadays drug discovery process. Scoring function is used as a fast method to estimate the docking results. In this thesis, a regional-defined genetic algorithm approach is developed to optimize the force-field based scoring function.
Human pregnane X receptor (PXR) is a nuclear receptor which is promiscuous in its affinity for ligands such as bile acid, steroid hormones, fat-soluble vitamins, prescription and herbal drugs, and environmental chemicals. In this thesis, the development and validation of in silico three-dimensional models for the pregnane X receptors is presented. These model aim at the screening of drug candidates for potential activity towards the PXR.
Potential side effects and toxicity of anti-trypanosomiasic active compounds were investigated using the VirtualToxLab. This technology identifies the binding mode of a small-molecule compound toward a series of 16 target proteins (nuclear receptors, cytochrome P450 enzymes, hERG, AhR) known or suspected to trigger adverse effects. The kinetic stability of the identified hits are evaluated by molecular dynamics simulations.