Accelerating Drug Discovery With Outcome-based Data Science and AI Application

L. Subramanian, S. Schweizer
United States

Keywords: AI, data science, machine learning, drug design, drug development


Typically, drug development takes several years. The power of new technologies can help to accelerate the whole process significantly enabling a faster market access. Saving cost and exploiting the capabilities of AI will allow to also target rare diseases leading to new markets. Data science and AI are developing at a fast rate enabling targeted drug design and molecular therapy. This talk will cover the innovation cycle in which new molecules are designed virtually through automated learning, predictive models are fine-tuned through an active learning cycle, and lead candidates optimized until the target therapeutic profile is met. This generative Virtual + Real cycle accelerates lead candidate design with improved quality, significantly reducing costs of experimentation, and advancing only the most promising candidates to clinical trials. With a clear focus on the end goal, our multidisciplinary experts collaborate with medicinal chemists at our customer sites to provide successful leads in a short time.