As a National Science Foundation Fellow, Dr. Keiser earned a PhD in bioinformatics in 2009 from UCSF, where he developed techniques, such as the Similarity Ensemble Approach (SEA), to relate drugs and proteins based on the statistical similarity of their ligands. Dr. Keiser also holds BSc, BA, and MA degrees from Stanford University. He subsequently cofounded a startup that brings these methods to pharmaceutical companies and to the US FDA. Dr. Keiser joined the faculty at UCSF in the Department of Pharmaceutical Chemistry and the IND in 2014, with a joint appointment in the Department of Bioengineering and Therapeutic Sciences. His lab investigates forward polypharmacology for complex diseases and the prediction of drug off-target activities.

The Keiser lab combines machine learning and chemical biology methods to investigate how small molecules perturb entire protein networks to achieve their therapeutic effects. In classical pharmacology, each drug was thought to strike a single note (in other words, "one drug hits one target to treat one disease"). However, it has been discovered that some drugs strike entire "chords" of targets at once, and this can be essential to their action. The Keiser group is tracing out this molecular music, not only in terms of new and useful therapeutic chords to treat neurodegenerative diseases, but also to identify the jarring notes that some drugs might unintentionally hit when they induce side effects.