Abstract: We are not maximally benefiting from the current explosion of information to discover more effective drugs. I will first present our efforts towards integrating and analysing Big Data to support cancer drug discovery at scale and the strengths and limitations of available data. I will then introduce a few examples of how we are using available (Big) Data and machine learning to better understand side-effects and expand the uses of available drugs in precision oncology.
Brief CV: Dr. Antolin is a PI and Miguel Servet Fellow at IDIBELL-ICO. He is an Organic Chemist by training (IQS, Universitat Ramon Llull). After working two years in pharma industry, he moved back to Academia to obtain a PhD in Pharmacoinformatics (Pompeu Fabra University). After seven years as postdoc at the Institute of Cancer Research (UK) and Columbia University (USA), he has recently returned to Spain to start his own team supported by a La Caixa Junior Leader Fellowship. His lab is interested in developing public chemical biology resources, harnessing the effects of drugs at a systems level in precision medicine and discovering new cancer drugs exploiting Big Data and AI.