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Structural Informatics & Molecular Level Analysis
- Goal: We apply artificial intelligence to chemical and protein structure data to gain insight into the molecular mechanisms of pharmacology.
Our current project areas include:
- Feature Family: We developed a system for modeling functional sites in protein structures, called FEATURE. FEATURE represents the local 3D environment around sites of interest using many physicochemical properties (at the atomic, molecular, residue, and secondary structure level) collected in radial, spherical volumes centered on the site.
- Deep Learning for 3D Structure: We apply the state of the art in deep neural networks to investigate binding relationships between proteins and their small molecule ligands.
- RNA Modeling: RNA is a key component in the regulation of biological systems. We seek to better understand the relationships between sequence, structure, and function of RNA in pharmacological contexts.
- Chemoinformatics: WSmall molecule drugs are the foundation of pharmacy. We seek to apply and advance the state of the art in chemical informatics methodologies, using small molecule structure data to predict properties, activities, and clinical outcomes. Our primary areas of interest are cancer, tropical diseases, drug metabolism, and dug-drug interactions.