An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations
Steven L. Rohall, Lydia Auch, Jonathan E. Gable, Jacob Gora, Johanna M. Jansen, Yipin Lu, Éric Martin, Margaret Pancost-Heidebrecht, Bill Shirley, Nikolaus Stiefl, Mika Lindvall
Abstract
Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying these sorts of approaches, we propose an additional approach: using AI to augment human intelligence. We have been working on a series of recommendation systems that take advantage of our existing laboratory processes, both wet and computational, in order to provide inspiration to our chemists, suggest next steps in their work, and automate existing workflows. We will describe five such systems in various stages of deployment within the Novartis Institutes for BioMedical Research. While each of these systems addresses different stages of the discovery pipeline, all of them share three common features: a trigger that initiates the recommendation, an analysis that leverages our existing systems with AI, and the delivery of a recommendation. The goal of all of these systems is to inspire and accelerate the drug discovery process.