Kinjal Mondal

This paper introduces XAMPGen, an interpretable generative model for discovering novel antimicrobial peptides using fine-tuned protein language models and explainable AI techniques.

This paper introduces STAMP, a machine learning framework for predicting antimicrobial peptide efficacy across bacterial species, achieving strong predictive performance and real-world validation.

This study uses molecular dynamics simulations and machine learning to analyze how mutations affect Smlt1473's binding to substrates, identifying key residues involved in substrate interaction.

The paper introduces two physically interpretable clustering metrics for evaluating MD simulation data, showing they align with physical intuition across different systems.