Yogesh Kalakoti

AFsample3 improves protein conformational sampling by generating and selecting multiple states, outperforming AlphaFold3 and AFsample2 in accuracy and diversity.

This paper develops a geometric deep learning method with mixture density models to predict protein-ligand binding conformations, showing improved performance over existing scoring functions.

AFsample2 improves prediction of protein alternative states and conformational ensembles by reducing co-evolutionary signals, enhancing structural diversity and model quality.

M-Ionic predicts metal ion binding sites from protein sequences using residue embeddings, achieving high accuracy for known ions and extending predictions to additional ions.