Fundamental AI Models for Drug Discovery
Building AI tools that help design therapies and predict how they will perform before clinical testing begins.
Drug development still depends on expensive, time-consuming cycles of trial and error. Better predictive and generative tools can help researchers make smarter decisions earlier.
AI has advanced target discovery and molecular design, but reliable prediction of developability, disposition, toxicity, and translational performance remains a major gap—especially for complex biologics.
Combine UW strengths in AI, pharmacometrics, and experimental data generation to build models that support therapeutic design, predict risk, and guide development decisions.
Faster design cycles, stronger go/no-go decisions, better prioritization of candidates, and a more scalable platform for future therapeutic discovery.
TBD
New Therapeutic Candidates
Advancing new medicines by applying AI-enabled design and experimental validation to promising therapeutic modalities.
The value of better tools is ultimately measured by whether they help produce better therapies for patients.
Peptides, antibodies, and other biologics can offer major therapeutic impact, but they are often difficult to design, optimize, and translate efficiently.
Use UW-developed AI models, high-quality datasets, and cross-disciplinary expertise to accelerate the design and refinement of therapeutic candidates across multiple disease areas.
Stronger therapeutic candidates, reduced R&D inefficiencies, and clearer translational pathways toward new therapies in areas such as oncology, infectious disease, and neurodegenerative disease.
TBD
Drug Delivery & Disposition for New Modalities
Predicting what happens to a drug in the body—and using that knowledge to improve safety, efficacy, and delivery.
Many therapies fail not because they lack promise, but because they do not reach the right tissues, do not remain effective long enough, or create unacceptable toxicity.
Existing tools often fall short for complex biologics and cannot reliably forecast in-body performance, drug-drug interactions, or delivery to difficult targets such as the brain.
AI-enabled PBPK models, pharmacometrics, organoid and organ-on-chip systems, and other translational approaches that improve prediction and reduce reliance on trial-and-error.
Improved clinical success through fewer failed trials, more precise dosing and delivery strategies, and earlier insight into how therapies are likely to perform in humans.
Developing scientists who can work across AI, drug design, pharmacokinetics, and translational research.
TBD
Research Resources
Mass Spectrometry Center
The Mass Spectrometry Center serves as a resource offering analytical and instructional expertise in contemporary mass spectrometric techniques and instrumentation with the primary aim of providing investigators with “hands-on” training and ready access to “state-of-the-art” instrumentation needed to develop and meet their research interests.
Pharmacokinetics and Modeling Lab
The PK Lab supports clinical studies through the analysis of drugs in an array of biological matrices (e.g. plasma, urine, lymphocytes), along with analysis and modeling of pharmacokinetic-pharmacodynamic (PK-PD) data. The PK Lab also has the capability to conduct various studies for the in vitro characterization of drug metabolism and transport.
Institute of Translational Health Sciences Pharmacokinetics and Modeling Lab
The Institute of Translational Health Sciences is dedicated to speeding science to clinic practice for the benefit of patients and communities throughout Washington, Wyoming, Alaska, Montana, Idaho, (WWAMI) and beyond. ITHS promotes this translation of scientific discovery to practice by fostering innovative research, cultivating multi-disciplinary research partnerships, and ensuring a pipeline of next generation researchers through robust educational and career development programs.