Michael Keiser, PhD

Associate Professor
Institute for Neurodegenerative Diseases
Phone: +1 415 886-7651
675 Nelson Rising Lane, Rm 416A
UCSF Box 0518
San Francisco, CA 94158
United States


What I do

Our group investigates how drugs affect entire networks of proteins in the body at once to achieve their therapeutic effects, via computational predictions paired with experimental testing.

My research expertise

systems pharmacology, machine learning, forward polypharmacology, computational chemical biology, neurodegeneration, adverse drug reactions, network structure, chemical-genetic epistasis

Professional background


Our lab investigates how small molecules perturb protein target networks to biological and therapeutic effect. In a forward polypharmacology campaign, we are using machine learning and computational methods such as the Similarity Ensemble Approach (SEA) to infer new combinations of targets underlying compound-induced phenotypes in cells and broader model systems. Thinking of each target as a musical note, we predict and test entire chords at a time via chemical-genetic epistasis experiments in models of complex diseases such as neurodegeneration.

These systems pharmacology inquiries tell us where current understanding of drug action is weak, and because we use human pharmacology to mine for mechanisms, we can apply the therapeutic chords we find directly to the prediction of patient drug responses, adverse drug reactions, and personalized medicine.

Research keywords

  • proteins
  • machine learning
  • Cerebral Amyloid Angiopathy
  • Organic Chemicals
  • Molecular Conformation
  • Amyloidogenic Proteins
  • Pharmaceutical Preparations
  • Small Molecule Libraries
  • Ligands
  • Preclinical Drug Evaluation
  • Drug Discovery
  • Adrenergic beta-1 Receptor Antagonists
  • Chemistry, Pharmaceutical
  • Plaque, Amyloid
  • Paroxetine