Agastya P. Bhati

Generative Active Learning for Molecular Design with REINVENT: Balancing Binding Affinity and Synthetic Accessibility

The results show that integrating synthesizability prediction into physics-based GAL workflows enables the efficient design of compounds that are at once chemically diverse as well as predicted to be strong binders and synthetically tractable, demonstrating efficient and practical computational drug design.