Liang Zhao

Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.

This work introduces a novel approach to transform the concept of "how to design chemical analogs like a medicinal chemist" into a primary training objective for generative models by focusing on matched molecular pair transformations (MMPTs) as the fundamental unit of chemical modifications.