May 3rd - 5th, 2022 | In-person & virtual
Applied BioMath Presence
Presentation: Applications of Machine learning in preclinical drug discovery
Presenter: Kas Subramanian, PhD, Executive Director, Modeling, Applied BioMath
Presentation time: Thursday, May 5th | 1:05pm
Abstract: The traditional process of drug discovery is complex, requiring the laborious experimental assessment of thousands of targets, hits, leads and candidates, making it expensive and time-consuming. Machine learning (ML) approaches provide methods that can improve the efficiency of the discovery process by formally integrating insights from data generated both in the public domain as well as internally. This talk will focus on case studies that demonstrate the application of ML to target validation and lead optimization and illustrate how machine learning methods can be used for decision making with quick informed predictions that can be rapidly validated by targeted experiments.