A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning
Researchers have developed a new method for learning small-molecule representations by leveraging protein-ligand co-folding. This approach, demonstrated with the Boltz2 model, shows that atom-level interaction data can significantly improve performance on tasks like ADMET prediction and molecular generation. The study found that these co-folding representations are complementary to existing methods and can enhance sample efficiency in molecular discovery. AI
IMPACT Introduces a novel pretraining paradigm for small-molecule foundation models, potentially improving drug discovery and molecular design.