Soham Choudhuri

This paper introduces a Transformer-based framework to predict full molecular dynamics trajectories from limited initial data, showing it captures long-range temporal structures and improves sampling compared to traditional methods.

The paper introduces a dataset and a graph-transformer model to predict IDR-protein binding affinity, achieving high accuracy and generalizing to designed binders.

This study uses single-cell transcriptomics and computational methods to identify key proteins in Plasmodium falciparum and predict potential drug molecules.

The paper investigates how gene length affects kinetic parameters like burst size and their impact on mean expression, suggesting implications for drug target selection.

This paper introduces HYDRA, a hybrid deep learning model for generating therapeutic peptides that bind to target receptors, with an application to malaria treatment.

This paper applies machine learning techniques to classify Plasmodium falciparum life cycle stages using single-cell transcriptomes, achieving improved classification through feature selection and multiple classifiers.