Abstract

Drug discovery is a complex, costly, and time-intensive process, often taking over a decade and billions of dollars to bring a single drug to market. Advances in artificial intelligence (AI) have transformed this field by accelerating early stages of drug discovery, including target and hit identification and lead optimization. However, the accuracy, generalizability, and interpretability of these predictions remain major challenges, specially when experimental data are limited.

In this talk, I will present our research on developing data-driven AI frameworks that can help predict and evaluate biomolecular interactions for drug discovery, focusing on interaction prediction and reliability estimation. I will first present our methods EquiPPIS and EquiPNAS for predicting protein–protein and protein–nucleic acid interaction sites using equivariant graph neural networks (EGNNs) and protein language models, and EquiRank, our approach for estimating the quality of protein–protein interfaces in multimeric structures, all of which can help target identification in early-stage drug discovery. Moving to hit identification, I will present how graph neural network-based frameworks can further help in predicting drug–target binding affinities and interactions to accelerate virtual screening and drug repurposing.

Together, these models show how AI-driven, symmetry-aware, and biologically informed frameworks can improve the efficiency and reliability of early-stages of drug discovery.

Speaker Bio

Md Hossain Shuvo is an Assistant Professor in the Department of Computer Science at Prairie View A&M University. His research lies at the intersection of computational biology, bioinformatics, machine learning, and data science. He develops data-driven computational frameworks to model and evaluate biomolecular interactions, with a focus on improving the reliability of predictions for biomolecules. His work has been published in leading journals and conferences, including Nucleic Acids Research, Bioinformatics, and ISMB. For more information, please visit https://mdhossainshuvo.github.io.