Article available at: https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00405. Matthew K. Matlock, Na Le Dang, and S. Joshua Swamidass Abstract: A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive ab initio quantum mechanics … Continue Reading ››

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A Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes  Dang, N. L., Hughes, T. B., Miller, G. P., and Swamidass, S. J. (2017). Chemical Research in Toxicology, DOI: 10.1021/acs.chemrestox.6b00336   Abstract: Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites, and … Continue Reading ››

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

Hughes, T. B. and Swamidass, S. J. (2017). Chemical Research in Toxicology, 30(2), 642–656.   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 ››

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