[Recorded] Prof. Tom Blundell - Structural and Computational Biology, AI/ML and Drug Discovery: Learning from Cancer to Target Mycobacterium Infections and Covid-19

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Title: The BII Lectures -  Structural and Computational Biology, AI/ML and Drug Discovery: Learning from Cancer to Target Mycobacterium Infections and Covid-19

Speaker: Prof. Sir Tom Blundell Director of Research in Biochemistry, University of Cambridge

Abstract:
Knowledge derived from genome sequences of humans and pathogens has the potential to accelerate diagnosis, prognosis and cure of disease. We are moving quickly into an era of precision medicine, not only in familial diseases where a mutation in a human gene is important, but also for understanding somatic mutations in cancer. Equally important, the genome sequences of pathogens, for example in tuberculosis or leprosy, can give clues about the choice of protein targets including those of existing drugs, repurposing of others, and the design of new ones to combat the increasing occurrence of drug resistance.

Structure-guided approaches, both in academia and large pharma, have informed drug discovery for five decades. More recently fragment-based structure-guided techniques have proved effective in lead discovery in Astex, a company I cofounded with Harren Jhoti and Chris Abell. Applications have been not only for classical enzyme targets such as protein kinases, but also for less “druggable” targets such as protein-protein interfaces. I will discuss progress in using these approaches for targets in cancer, in mycobacterial tuberculosis, abscessus and leprae infections, and in Covid-19.

I will also review our computational approaches, using both statistical and machine learning methods. One area has been applications for understanding ligand binding and another mechanisms of drug resistance. These have led to new ideas about repurposing and redesigning drugs.

About Speaker: