All posts by Tyler Hughes

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Hughes, T. B. and Swamidass, S. J. (2017). Chemical Research in Toxicology,  DOI: 10.1021/acs.chemrestox.6b00385 TOC   Abstract: Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins,causing cancer … Continue Reading ››

Update: Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

This is a minor update to figure 10 in a recently published paper: Original Paper. The revised figure displays both the RMSE between each method and perfectly scaled prediction, and the R2 values for the Pearson correlation of the best-file line for that method. As measured by RMSE, the three methods have the same relative value as in … Continue Reading ››

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

Citation: Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. Tyler B. Hughes, Grover P. Miller, and S. Joshua Swamidass.  ACS Central ScienceDOI: 10.1021/acscentsci.5b00131 Abstract:  epox2 Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise … Continue Reading ››

Combined Analysis of Phenotypic and Target-Based Screening in Assay Networks

Citation: Swamidass, S. J., Schillebeeckx, C. N., Matlock, M., Hurle, M. R., & Agarwal, P. (2014). Combined Analysis of Phenotypic and Target-Based Screening in Assay Networks. Journal of biomolecular screening, 1087057114523068. Abstract: Small-molecule screens are an integral part of drug discovery. Public domain data in PubChem alone represent more than 158 million measurements, 1.2 million molecules, and 4300 assays. … Continue Reading ››

Sharing Chemical Relationships Does Not Reveal Structures

Citation: Matlock, M., & Swamidass, S. J. (2013). Sharing Chemical Relationships Does Not Reveal Structures. Journal of chemical information and modeling, 54(1), 37-48. Abstract: In this study, we propose a new, secure method of sharing useful chemical information from small-molecule libraries, without revealing the structures of the libraries’ molecules. Our method shares the relationship between molecules rather … Continue Reading ››