Applying Active Learning in Drug Discovery

While computational methods have become a mainstay in drug discovery programs, many calculations remain too time-consuming to be applied to large datasets. AL—a machine learning technique that iteratively directs searches—enables the application of computationally expensive methods such as RBFE (relative binding free energy) calculations to datasets containing thousands of molecules. Additionally, AL can be applied to virtual screening allowing for the rapid evaluation of billions of molecules with remarkable efficiency. In this presentation, Pat provides an overview of two practical applications of AL in drug discovery. 00:00 Welcome and introduction 08:11 Overview of Drug Hunter features 11:50 Presentation 49:44 Q&A If your company doesn’t have a subscription yet, reach out here to inquire about access: https://drughunter.com/plans Presented by Pat Walters, Ph.D. with host Dennis Koester, Ph.D. Director of Industry Research and Relations, Drug Hunter. Recorded on February 6th, 2025, as a Drug Hunter Flash Talk. Sign up to be notified of future sessions here: https://share.hsforms.com/14dUq-XP5Qfe_nHmL3WnWbgbm5dt Sign up for our free newsletter here: https://drughunter.com/newsletter-sign-up