Although no single drug has been designed solely by computer techniques, the contribution of these methods to drug discovery is no longer a matter of dispute. All the world’s major pharmaceutical and biotechnology companies use computational design tools. Computer-aided drug design represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in-silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this course the bases of the computer-aided drug design will be explored, and the lectures will be accompanied by laboratory exercises.
COURSE OUTLINE a) Molecular dynamic simulations; b) Pharmacophore-based drug design c) Consensus docking-based drug design; d) Homology modeling techniques; e) Artificial intelligence methods applied to the drug discovery field. The course aims at providing the students with advanced understanding of computational modeling in the area of drug discovery. After finishing the course the students will have: • Advanced understanding of ligand-protein interactions. • Be familiar with a range of ligand and structure based computational methods. • Performed computational modeling tasks using state of the art software.