3 papers
cs.LG2026
APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries
Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley +5
Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts. However, their large size presents a challenge for virt…
cs.LG2025
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
cs.LG2024
Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization
Ryan Park, Darren J. Hsu, C. Brian Roland +5
Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a m…