Sarala Padi

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.

The paper develops a meta-model that combines multiple machine learning models to predict ADMET properties, outperforming existing methods on a benchmark dataset.