Traditionally, drug targets are found by scouring scientific publications for insights into molecular pathways or known causative genetic variants, linked to disease. The failure rate of drug candidates in the clinic, even in relatively late-stage clinical trials, is quite high and is extremely costly. Fundamentally, finding better targets will lead to development of better medicines. In this episode, we discuss how artificial intelligence (AI) and the increasing availability of complex biological datasets can be leveraged to identify molecular targets. Machine learning models trained on large amounts of data allow researchers to differentiate between states or conditions more specifically to predict disease-relevant targets.

Guest: Avantika Lal, PhD, Senior Genomic Data Scientist, insitro

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