Aarya Venkat

Glydentify predicts donor substrates for glycosyltransferases using deep learning, achieving high accuracy and providing explainable insights into enzyme-substrate relationships.

The paper introduces a toolkit using machine learning to predict ice binding protein potential, activity, and expression from sequences, achieving high accuracy and scalability.

This paper introduces an alignment-free protein classification framework using sequence embeddings to infer evolutionary relationships and functional motifs across diverse protein superfamilies.

The paper introduces QSMART, a model that predicts protein kinase inhibitor response by combining statistical and neural network methods, achieving high accuracy and feature explainability.

The paper uses evolutionary analysis of GT-A fold sequences to identify core components and hypervariable loops contributing to catalytic diversity, and develops a machine learning classifier for donor specificity prediction.