Rana Ahmed Barghout

This paper evaluates residue-level attributions in protein language models using synthetic fitness landscapes and identifies interventions that improve attribution accuracy.

This paper introduces kinGEMs, a framework that integrates deep learning-predicted kinetic parameters into genome-scale models to improve metabolic modeling accuracy and scalability.

CPI-Pred predicts compound-protein interaction parameters using deep learning, outperforming existing models on unseen compounds and enzymes.