Vice President, SRI Biosciences
Peter Madrid, Ph.D., is head of the Biosciences Division Applied Research group in Menlo Park. His team focuses on developing innovative technologies to accelerate the discovery of drugs and diagnostics for significant unmet medical needs. The group has produced a pipeline of drug candidates, primarily in the areas of cancer and infectious diseases.
Dr. Madrid has led multidisciplinary teams conducting research projects on infectious disease drug discovery, automated chemical synthesis, high-throughput screening methodologies and AI-driven drug discovery. He has published numerous papers in the field of anti-infectives drug discovery and is an inventor on several patents on drug discovery technologies.
He received his Ph.D. in chemistry and chemical biology from the University of California, San Francisco, and his B.S. in chemistry from the University of California, Santa Cruz.
View Dr. Madrid’s publications on PubMed and Google Scholar.
Recent publicationsmore +
Accelerating space radiation countermeasure development through drug repurposing
The discovery of safe and effective radiation countermeasures for long-duration spaceflight is challenging due to the complexity of the space radiation biology and high safety requirements.
Mega-High-Throughput Screening Platform for the Discovery of Biologically Relevant Sequence-Defined Non-Natural Polymers
We developed a novel technology for screening and sequencing libraries of synthetic molecules of up to a billion compounds in size.
Expanding the Metabolite Mimic Approach to Identify Hits for Mycobacterium Tuberculosis
A Systematic Screen of FDA-Approved Drugs for Inhibitors of Biological Threat Agents
The feasibility of repurposing existing drugs to face novel threats is demonstrated and this represents the first effort to apply this approach to high containment bacteria and viruses.
Systematic Discovery of Synergistic Novel Antibiotic Combinations Targeting Multidrug-Resistant Acinetobacter Baumannii
Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis
Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators.