Integrating uncertainty into drug-target deep learning
The Keiser lab combines machine learning and chemical biology methods to investigate how small molecules perturb protein networks to achieve their therapeutic effects. Michael Keiser joined the UCSF faculty in the Dept. of Pharmaceutical Chemistry and the Institute for Neurodegenerative Diseases as an Assistant Professor in 2014, with joint appointments in the Dept. of Bioengineering & Therapeutic Sciences and the Institute for Computational Health Sciences. Before this, he co-founded a startup bringing systems pharmacology methods to pharma and the US FDA. During his bioinformatics Ph.D. at UCSF as a NSF Fellow, Michael developed techniques to relate drugs and proteins from the statistical similarity of their ligands, such as the Similarity Ensemble Approach (SEA). He also holds B.Sc., B.A., and M.A. degrees from Stanford University.