Drug development is among the slowest, most failure-prone processes in modern science, with . Today, artificial intelligence methods have accelerated the first step — plucking promising molecules out of endless possibilities — but countless challenges remain. A successful drug must be not only safe and effective, but also able to bypass the body’s defenses and reach the right target.

Most drug candidates fail such optimizations. That’s where a new research institute at the ����Ӱ�Ӵ�ý has focused its attention. Housed in the UW School of Pharmacy, the brings together experts in artificial intelligence, drug discovery, pharmacology, data science and biotechnology to ease the bottleneck between promising molecules and successful drugs. 

The Institute opened in July 2026 and is led by three UW faculty members: , an associate professor of medicinal chemistry who oversees the Institute’s AI-enabled molecular design; , the Milo Gibaldi Chair of Pharmaceutics and expert in drug metabolism and disposition; and , a professor of psychiatry and behavioral science in the UW School of Medicine, who leads drug discovery, translation and commercialization efforts.

UW News spoke with the three co-directors about why drug candidates fail, how AI is speeding drug development and how I2D3 hopes to help get drugs to market more quickly.

What separates a promising molecule from a full-fledged drug? What properties need to be considered, and how can a developer work toward them?

Gaurav Bhardwaj: It really depends on the disease indication you are targeting and the therapeutic modality. Let’s say you have a promising molecule that interacts with the disease-causing protein. Delivery becomes equally important — do we need an orally delivered drug? Do we need to cross the blood-brain barrier? If the disease requires daily dosing, then injectable or IV methods aren’t optimal. If it’s delivered orally, then the molecule needs to be able to get across the gut barrier, and also needs to be stable enough that it doesn’t get chewed up by the body. It also needs to stay in the body for a reasonable time. A successful drug molecule has to meet all these and more criteria, and ultimately all these criteria are encoded by the sequence and structure of the molecule.

The Institute is devoted to aspects of drug development that are often overlooked. What problem do you see the Institute being able to help solve? 

GB: Traditional drug discovery and development is a trial-and-error-based process. Either you find a useful molecule in nature and spend years optimizing it for human use, or you create many random combinations of molecules and hope that one of them has the function you need. Both of these approaches are highly unsuccessful, which has created a bottleneck.

Now the field is also focusing on an idea called rational drug design. It started long before AI but is now becoming even more common. People are using AI methods to design new molecules. However, a lot of that work has focused on the first step — finding a molecule that binds to a specific protein, or has a specific function in the body. That’s still not a drug, it’s just more candidates.

The bottleneck has now shifted. It’s no longer finding that first molecule, but now, how do you add all the other drug-like properties? That’s what the Institute is trying to do. Let’s build the models that ultimately make molecules that are going to be successful all the way through the drug development pipeline. 

Marco Pravetoni: I see our work also as accelerating discovery. I work on substance use disorders, and my lab develops vaccines, antibodies and next-generation antibody-like molecules that target drugs in the body. With these new tools, instead of working to design 10 antibody candidates in a lab, we could design 1,000 or more, and then we can accumulate enough data to reduce any risks, so that what we bring to clinical trials is more likely to be successful. AI can do a lot of that.

How can you make it more likely that a drug candidate succeeds in trials? 

Nina Isoherranen: Part of it is predicting what’s going to happen to a drug in humans before it’s ever given to humans. That should increase the success rate and eliminate the waste of doing a lot of unsuccessful trials. 

We can also build machine learning and AI approaches to predict drug disposition in an individual person. What we talk about today are ‘digital twins,’ which refers to a computational model of the individual patient and their characteristics. For example, how does your kidney function? What is your body mass index? And so forth. Then we generate a digital version of you. We can then predict how a certain drug would behave in your body and build the best strategy. 

There’s also an access-to-treatment question here. Pregnancy is a great example — we often don’t know how drugs work in pregnant women because we’ve never done trials. To be safe, we say that pregnant people shouldn’t take those drugs, but that means they don’t have access to a potentially hugely beneficial medication. If we can use AI and machine learning to predict how pregnant people respond to medications and how their bodies handle drugs differently from nonpregnant people we can make more medications accessible

Now with AI and machine learning, I think we can get to a place where we can truly sample the full space of possibilities. 

How can the methods you’re building help with these individualized treatments? 

NI: We know that drugs behave differently in different people. Even if we give them the exact same drugs and concentrations, people may still have different responses because of factors inherent to our bodies.

During drug development the candidate drug needs to be studied to see responses in different populations. Before you get a drug approved, you need to understand how liver disease, for example, is going to change exposure to that drug and whether you need to change the dosing. There’s a lot of guidance on drug interactions. Pharmacists manage drug interactions all the time, but it gets very complicated when you combine multiple patient factors. Now, if we have good predictive tools, we can predict what’s going to happen without having to do trials. 

The ultimate goal here is to be able to predict, using model computational tools, what’s going to happen in individual humans before you ever give them a drug. What’s the right dose? The right timing? 

UW has established itself as a leader in these fields already. I’m thinking especially of the UW Medicine , whose director, , recently won the Nobel Prize in Chemistry. How does I2D3 fit into the broader UW ecosystem? 

MP: IPD is a world leader in designing novel proteins, and the UW also has outstanding capabilities in clinical testing and implementation through the . However, there remains a critical translational space between discovery and clinical application —  one that focuses on the pharmaceutical development needed to turn promising innovations into viable therapeutic products. That’s where I2D3 can play a leading role.

For example, when researchers at IPD develop a new protein, I2D3 can partner with them early to address formulation, manufacturability, stability, delivery, and other key pharmaceutical considerations that are essential for advancing a discovery toward the clinic and ultimately the marketplace. I2D3 would serve as a core translational partner, helping bridge the gap between innovation and implementation.

IPD brings unmatched strengths in protein design, ITHS provides expertise in clinical translation, and I2D3 contributes the drug development and pharmaceutical sciences capabilities needed to move discoveries across the translational continuum. Together, these organizations can create a powerful and highly integrated ecosystem.

For more information, visit . To reach the researchers, contact Alden Woods at acwoods@uw.edu.

With $4 million in matching funds from the National Institutes of Health, the ����Ӱ�Ӵ�ý has created a new integrated center to match biomedical discoveries with the resources needed to bring innovative products to the public and improve health.

“The ����Ӱ�Ӵ�ý and regional partner institutions produce some of the most exciting biomedical discoveries and technologies in the world, but we always find it challenging to support their product development as they move into the early commercialization phases,” said the new center’s executive director , a professor in the UW School of Pharmacy.

With well over a billion dollars in research funding annually, the ����Ӱ�Ӵ�ý is an engine of discovery and generates more than $15 billion in the state’s economy.

����’s newly funded Washington Entrepreneurial Research Evaluation and Commercialization Hub (WE-REACH), with an annual budget boosted to $1.4 million by contributions from other partners, is organized to mentor and support biomedical entrepreneurs as well as provide project funding to fuel four to six biomedical startups a year with up to $200,000 each.

Those projects will include innovative disease treatments, new drugs, diagnostics, genetic testing and health technologies. Ho said the center will support innovation steps not typically supported by research grants, such as human clinical trials or the development of and access to products.

In addition to the NIH, WE-REACH partners include the UW ,��, the  and the . Innovators will receive guidance from multiple academic departments and regional institutions. Those institutions include the Fred Hutchinson Cancer Research Institute, Seattle Children’s and other universities in the five states that make up the �����𲵾��Dz�.

“We are delighted to welcome WE-REACH as a partner,” said , executive director of the Institute of Translational Health Sciences. “At ITHS we are committed to accelerating the translation of discoveries to the clinic. WE-REACH investigators will be able to leverage ITHS programs and resources, so they can help us in our mission to improve health in our communities. This is a very exciting area of translation that we are happy to support.”

WE-REACH is one of five national commercialization  selected for funding by the NIH in 2019.

“The journey of biomedical discoveries to products that improve people’s health is expensive and risky. The process requires strategic investment of know-how as well as financial support from public-private partnerships,” said Ho.

“Spinning life science innovations out of research institutions requires expertise and funding that is hard to source in the academic environment,” adds , assistant vice president, innovation development at CoMotion, ����’s collaborative innovation hub. “WE-REACH builds on the infrastructure CoMotion has developed, including our gap fund and training, to provide critical resources needed to de-risk promising technologies into preclinical and clinical development.”

The new center will be located in the South Campus Center on the ����Ӱ�Ӵ�ý’s Seattle campus and at the Institute of Translational Health Sciences in UW Medicine South Lake Union. It will be staffed by Professor Rodney Ho, Executive Director; Terri Butler, Associate Director of Outreach and Partnerships; Matthew Hartman, Coordinator; Christine Jonsson, Administrator; and new hires in project management and technology management roles.

For information on the new center and how to submit a grant proposal,��please contact Matthew Hartman at WEREACH@uw.edu or 561-339-0676. The next round of grant funding requires a declaration of intent by Feb. 7 and complete submission by May 1.

NIH Grant: 1 U01 HL152401-01

Learn more about the ����’s Population Health Initiative: a 25-year, interdisciplinary effort to bring understanding and solutions to the biggest challenges facing communities.