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 ››
At this time (March 2017), we are looking for graduate students, postdoctoral fellows,
and staff scientists
to work on machine learning projects in medicine, chemistry and biology.
We are looking to train scientific leaders, capable of innovative computational research. Our group is working in close collaboration with leading clinicians and scientists on biomedical imaging, drug toxicity modeling … Continue Reading ››
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
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.
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 ››
Dr Greg Bowman
and I invite you to the Center for Systems Biology and Engineering seminar series for 2016-2017. The title of this series is “Mathematically Modeling Biological Systems
We hope for robust discussion, including critical feedback and scientific scrutiny of the presented work. This series is structured as a true seminar - not a colloquia … Continue Reading ››