Like Robert Frost’s indecision about which essentially equivalent path to take, change often starts small but the simple choice of one path over another can eventually lead to big changes that become widely accepted and adopted.   Such is the case with the growth of molecular modeling and informatics and its impact on the drug discovery process.  It began more than half a century ago with the concept of mining specific lessons from focused structure-activity data (QSAR) and has progressed to the unsupervised deep learning (ML) approaches which are currently in vogue.  In between, a host of ligand-based, structure-based, and knowledge-based approaches have been successfully employed to accelerate and/or alter the path of drug discovery toward marketed medicines.  What’s next?  Surely, we can only go onward and upward from here.

“I took the road less traveled by … and that has made all the difference”

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.