Rethinking Drug Discovery in the Era of Digital Biology
, Insitro
Modern medicine can now treat some of the most burdensome diseases. At the same time, developing new therapeutics is becoming increasingly harder, because the drug development process involves multiple long and complex steps that often ultimately fail. At insitro, we aim to combine high-throughput data production with machine learning models to help predict the outcome of these experiments. We're bringing together high-quality data from human cohorts, while also developing cutting-edge methods in high-throughput biology and chemistry that can produce massive amounts of in vitro data relevant to human disease and therapeutic interventions. Those are then used to train machine learning models that make predictions about novel targets, coherent patient segments, and the clinical effect of molecules. Our goal is to develop a new approach to drug development that uses high-quality data and ML models to design transformative therapies that help more people, more quickly, and at a lower cost.