Various in silico methods are employed to predict toxicity in pharmaceutical R&D. The methods can range from simple structural alerts all the way to detailed mechanistic modeling of biological systems. In my talk I will briefly go over some of these methods with a focus on structure-activity relationships, network-chemical similarity approaches and dynamic systems modeling. The talk will cover issues around how these models are built, their applicability and their impact on the pharmaceutical pipeline.I will discuss how the quality of predictions made influences decision-making.
About the Speaker: Dr. Kalyanasundaram Subramanian from Strand life sciences, popularly known as 'Kas', leads Strand's scientific and technical programs and coordinates the cross-divisional efforts in R&D. Kas's interests lie in the field of ADMET modeling and molecule design using machine learning and systems biology techniques. He has over a decade of experience in modeling biological systems.
An IIT - Bombay alumnus, Kas went on to complete his M.S. in Chemical Engineering from the State University of New York College at Buffalo. Equipped with a Ph.D. in Biomedical Engineering from Johns Hopkins University, Kas took up the position of Senior Scientist at Genetic Therapy Inc (Novartis) between 1997-2000, where he helped set-up a group to perform research in synthetic and hybrid vectors for gene delivery. Prior to Strand, Kas headed the Collaborative R&D group for immunology products at Entelos.
