David H. Sherman

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My group is interested in the discovery and characterization of natural product molecules for pharmaceutical development. Our pipeline relating to data science begins with microbial genome sequencing, mining of natural product biosynthetic gene clusters, integration of LC-MS/MS, and biological activity data. A major challenge in the field of natural product sciences is to 3-dimensional structures of complex metabolites based on genome sequencing data. Advancing this challenge will provide access to millions of new chemical structures with potential important disease fighting activities derived from cheap, rapid, next-gen sequencing technologies. Moreover, accurate prediction of complex natural product structures from genome sequencing data would then allow virtual screening of compounds against important disease targets (e.g. ribosomes, kinases, receptors) to begin the drug discovery process. It would also enable virtual medicinal chemistry to optimize molecules for disease target binding and optimal bioavailability and pharmacokinetic properties.

Rudy J. Richardson

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Applications of computational tools for molecular modeling (Discovery Studio, ICM-Pro, MOE, and YASARA) and data science (ADMET Predictor, KNIME, Origin Pro, Prism, Python, and R) to computational toxicology, drug discovery, homology modeling, molecular dynamics, and protein structure/function prediction. Current special interests include therapeutics for neurodegenerative disorders (Alzheimer’s, Parkinson’s, and motor neuron diseases) and infectious diseases (COVID-19).

3D alignment of acetylcholinesterase (AChE) from mouse (magenta) and electric eel (gray) showing the amino acid residues of the catalytic triad.


Accomplishments and Awards