AI4SCIENCE@SCAI Seminar #2 - "A Multi-Task Deep-Learning Model for PPI Network Reconstruction and Interface Prediction "
AI4Science: A New Paradigm for Scientific Discovery
On March 9, Sorbonne University hosted the launch day of the AI4Science initiative within SCAI (Sorbonne Cluster for Artificial Intelligence). The event brought together researchers from multiple disciplines, institutional representatives, and industrial partners to explore how artificial intelligence is transforming scientific discovery. Beyond a mere overview of projects, the day highlighted a deeper evolution: the emergence of a new research paradigm where AI and scientific reasoning are increasingly and inextricably intertwined.
Following the success of our inaugural launch event, SCAI is proud to announce its second seminar.
The topic of the second session is "A Multi-Task Deep-Learning Model for PPI Network Reconstruction and Interface Prediction" - by Sara Rescalli, PhD at CQSB.
Protein–protein interactions (PPIs) are central to cellular organization and regulation, shaping the molecular pathways that drive most biological processes. Yet experimental characterization of PPIs remains costly, time-consuming, and incomplete, leaving critical gaps in our knowledge of which proteins interact and where these interactions occur at the residue level.
We evaluate X-PAIR on established benchmarks for interaction and interface prediction, and further assess its generalization from an evolutionary perspective, testing whether predictions remain reliable across species phylogenetically distant from those seen during training.
Overall, X-PAIR demonstrates how AI can address challenging open problems in biology, extending interaction and interface annotation to organisms where experimental data and structural information remain limited.
To participate at the seminar, the registration is mandatory.