Designing putative drugs for a specific protein target represents one of the main challenges for medical and pharmaceutical research, with obvious impacts for the drug discovery industry.
Since the 3D structure of proteins have become available, biologists, chemists and physicists have tried to make use of high resolution structural data to get reliable predictions of putative ligand structures that can interact with the bio-macromolecular target as well as to evaluate the strength of the protein-ligand interactions. The exponential growth of experimental structural models of macromolecular targets, obtained by X-ray crystallography and cryo-electron microscopy and the development of high performing computational methods for drug design provide a wealth of information and tools that can be exploited in a structure-based drug development pipeline. In parallel, the striking recent developments in artificial intelligence algorithms for computational protein modeling can generate reasonably accurate models for the proteins whose structure is not yet known, further enlarging the pool of available targets and thus significantly contributing to drug design efforts.
The topics will cover the basics principles of macromolecule-ligand interactions, the role of macromolecular crystallography, cryo-electron microscopy, and artificial intelligence-based structure prediction in drug design, small molecules docking, biophysical analysis, ligand fitting into electron density maps, fragment-based drug design, and molecular dynamics approaches. To reinforce and extend the lectures, a number of hands-on computational sessions will be implemented, giving the students the opportunity to use some of the software available and practice what has been presented.