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Michael Keiser, PhD

Michael Keiser, PhD

Assistant Professor of Pharm Chem, BioEngineering & Therapeutic Sciences

As an NSF Fellow, Dr. Keiser has a PhD in bioinformatics from UCSF, where he developed techniques, such as the Similarity Ensemble Approach, to relate drugs and proteins based on the statistical similarity of their ligands. Dr. Keiser also holds BSc, BA and MA degrees from Stanford. He cofounded a startup that brings these methods to pharmaceutical companies and to the US FDA. The Keiser lab investigates forward polypharmacology for complex diseases and the prediction of drug off-target activities, combining 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 strikes a single note (“one drug hits one target to treat one disease”). The Keiser group is tracing out molecular music – new and useful therapeutic chords to treat neurodegenerative diseases.

Research Overview:

Small molecule therapeutics with protein network perturbations
In classical pharmacology, drugs struck single notes, where one drug would hit one target to treat one disease. But drugs frequently modulate entire target “chords” at once, and this can be essential to their action. The Keiser lab is decoding this molecular music, both in terms of new and useful chords for the treatment of complex diseases, and also to identify the jarring notes that existing drugs unintentionally hit when they induce side effects. Michael is also uncovering the biological roots of Alzheimer’s disease.

Computational health science interests: