Open access
Jul 2026
Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.
The results show that the use of suitable encoder-regressor pairs together with embedding-level mix-up augmentation improves model generalizability without requiring SMILES-level augmentation, and could be applied more broadly to IC50 prediction for other kinase inhibitors.
Ju Hyung Lee, S. Choi, Utku Ozbulak et al.
· Journal of Cheminformatics · 0 citations