At Dr. Imtaiyaz Hassan’s laboratory, we integrate precise experimental measurements with advanced computational approaches to elucidate protein structure–function relationships and translate those findings into therapeutic candidates. Our AI-driven drug discovery platform employs machine learning algorithms to screen extensive chemical libraries, predict binding affinities, and prioritize compounds for synthesis. In parallel, we explore natural product–based therapeutics drawn from diverse biological sources, coupling biophysical assays with data-driven lead optimization to identify scaffolds that combine high specificity with favorable pharmacokinetic profiles.

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Our investigations into kinase inhibition and cell signaling focus on the molecular mechanisms that regulate cellular proliferation and survival. By combining all-atom molecular dynamics simulations with high-throughput enzymatic assays, we characterize active and allosteric sites, define key interaction networks, and engineer inhibitors tailored to disrupt aberrant signaling in oncogenic pathways. These efforts feed directly into our cancer biology and therapeutics initiatives, where candidate molecules are evaluated in a range of cell-based models to assess potency, selectivity, and resistance potential, thereby expediting the transition from bench to preclinical validation.

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In the field of neurodegenerative disease research, we employ deep learning-enhanced bioinformatics to analyze sequence and structural features that drive protein misfolding and aggregation in disorders such as Alzheimer’s and Parkinson’s disease. Our computational pipelines identify small molecules capable of stabilizing native conformations or preventing toxic oligomer formation. Through rigorous experimental validation and transparent dissemination of data, analytical workflows, and code, our laboratory aims to address critical challenges in biomedical research and accelerate the development of effective treatments.