Andrea Bortolato

This paper identifies selective SV2C ligands using AI-enhanced virtual screening and experimental validation, finding 22 active molecules with high selectivity over SV2A and SV2B.

This paper identifies a novel non-canonical RT-loop degron in VAV1 and demonstrates how conformational restriction and computational modeling enhance molecular glue potency for proteasomal degradation.

MAHLER predicts antibody-antigen dissociation kinetics using a hybrid machine learning and physics approach, achieving screening-grade accuracy and fast computation.

The paper improves binding affinity predictions by using synthetic data, showing that combining synthetic and experimental data enhances model performance.