Saikat Dhibar

Transferable Collective Variable to accelerate Protein-Ligand (Un)Binding Transitions via Explainable Machine Learning and Intriguing Role of Ligand Solvation

This paper introduces an interpretable machine learning method to derive optimized collective variables for protein-ligand binding-unbinding transitions, achieving accurate free energy estimates and residence times with low computational cost.

Correlations among hydrogen bond fluctuations in the apo state are enough to reveal allosteric networks in proteins

This paper introduces a computational method to identify allosteric networks in proteins by analyzing hydrogen bond fluctuations in the apo state, demonstrating its effectiveness across three proteins.

Prediction of Enzyme function using interpretable optimized Ensemble learning framework

This paper introduces SOLVE, an interpretable ML framework for enzyme function prediction using primary sequence data, achieving high accuracy and identifying functional motifs.