Francesco Trozzi

This study uses AlphaFold2 Steering and machine learning to model kinase conformations and identify state-specific inhibitors, achieving a 53% hit rate in virtual screening and validating conformation-specificity for FLT3 ligands.

SSnet predicts protein-ligand interactions using secondary structure features, outperforming existing models and revealing key structural elements for ligand binding.