The Ondrechen Research Group (The O.R.G.) is based in the Department of Chemistry and Chemical Biology at Northeastern University in Boston, USA. Our work spans the areas of theoretical and computational chemistry, computational biology, bioinformatics, protein design, and drug discovery. We have collaborators in experimental chemical biology, experimental biophysics, protein engineering, medicinal chemistry, mathematics, and computer science. Areas of interest include functional genomics – predicting the biochemical functional roles of gene products (proteins), protein engineering, providing computational guidance for drug discovery, and understanding the fundamental basis for enzyme catalysis. The Ondrechen group has developed methods to predict protein function from 3D structure. Our THEMATICS method (see Ondrechen et al., Proc. Natl. Acad. Sci. USA 98, 12473, 2001) requires only the structure of the query protein and thus works for proteins that bear no resemblance to previously characterized proteins. Partial Order Optimum Likelihood (POOL) is a new Machine Learning method and top-performing predictor of functionally important residues in proteins (see Tong et al., PLoS Comp. Biol. 5(1): e1000266, 2009). Structurally Aligned Local Sites of Activity (SALSA) is a biochemical function predictor for protein structures [see Wang et al. BMC Bioinformatics, 14(Suppl 3):S13 (2013)]. Our latest protein function predictor is graph theory based (“Functional classification of protein structures by local structure matching in graph representation,” Caitlyn L. Mills, Rohan Garg, Joslynn S. Lee, Liang Tian, Alexandru Suciu, Gene Cooperman, Penny J. Beuning, Mary Jo Ondrechen, Protein Science 27, 1125-1135 (2018). PMID: 29604149). Our latest project is the characterization and drug discovery for SARS-CoV-2 (novel coronavirus) protein targets.