Por Irina Moreira (CNC-UCoimbra).
Despite technological advances, the conventional process of drug discovery and development continues to show limited therapeutic efficacy of drug-leads due to a partial understanding of disease pathophysiology, overall deficiency in developing therapeutics that target overlapping dysregulated pathways, and the choice of therapeutically irrelevant drug targets. The difficulty lies in the interpretation and mining of an ever-growing and overwhelming wealth of diverse data that are disparate from global systemic approaches with an increased granularity of evidence, of which large searchable databases already exist. Determining the relative importance of different pieces of evidence when combining the available information to characterize successful targets for drug discovery is another challenge. Given the latest advances in the field of Artificial Intelligence (AI), this enormous task can now be pursued.
We are following an AI-based approach that combines mainly biophysics/structural information and omics to characterize and identify target, drug, and drug-target interactions. In this webinar we will provide some examples of big-data and/or AI-driven modelling frameworks already developed in our group.
Transmissão via Zoom.
