Award to help researchers catalyze AI-driven discovery for public good

Seventy countries and thousands of researchers and citizen scientists. That’s how far Virginia Tech computer science researcher Debswapna Bhattacharya’s publicly available biomedical artificial intelligence (AI) platform has already spread.

From well-funded labs in the U.S. to undergraduate students in developing countries, anyone with an internet connection can, and has, run sophisticated molecular analyses using a simple, web-based platform hosted in the Department of Computer Science.

Bhattacharya recalled one inquiry he received from Africa.

“He actually started using this web server that we developed when he was an undergraduate student, and he carried out a project all by himself, came up with a paper as a single author, submitted it to a preprint server, and then sent me that paper, saying, ‘Using your server, I actually carried out this work,’” Bhattacharya said.

That student has gone on to graduate studies in the U.S.

Now Bhattacharya, associate professor of computer science, has received a five-year, $2.1 million National Institutes of Health (NIH) Outstanding Investigator Award to build on this groundbreaking work to develop innovative AI approaches to decode disease and find treatments. The grant program supports basic research related to disease diagnosis, treatment, and prevention, providing funding stability to push scientific discovery forward, faster.

“This is what computer science looks like at its best — advancing discovery while improving lives on a global scale,” said Christine Julien, head of the Department of Computer Science. “It reflects our deep commitment to using technology in service of people, wherever they are.”

For Bhattacharya, that reach is not a byproduct of the work — it’s a mission. 

“We are fortunate to have a lot of resources, like internet connectivity and so on,” he said. “The important thing is touching people's lives in places that are not blessed to have these resources.”

Seeing is believing

Bhattacharya’s team focuses on proteins and RNA — the biological machines of human and animal life — and uses deep learning, a form of AI, to predict how these molecules are structured and how they function at the atomic level. 

These molecules are incredibly complex, but if scientists can map their 3D shapes accurately, they can spot places to target treatments and begin developing new drugs for disease.

Unlike the massive image or text datasets used to train AI systems, biological datasets are often scarce, which can cause deep learning models trained on them to make unreliable predictions. To address that, his team is building “biology-guided” and “biophysics-informed” AI systems that incorporate established scientific principles from chemistry and physics, making the models both more accurate and more interpretable.

“We’re training on structural data that experimentalists have painstakingly built over 70 or 75 years. We’re incredibly lucky to have it,” Bhattacharya said. “Now our job is to use deep neural networks to fill in the gaps.”

The long-term goal is to better understand how biomolecules interact, particularly RNA and protein-RNA systems, which remain harder to model than proteins alone.

Three doctoral students are helping to solve this problem. 

• Sumit Tarafder, a fourth-year doctoral student, is working on the RNA modeling part of the project.
• Xingyue Feng, who joined the team last year, is working on proteins.
• Xinyu Wang, a member of the lab since 2024, is the bridge. She is bringing the RNA and protein work together.

RNA and protein molecules present a daunting challenge because they don’t stand still. Because the shape of a molecule affects its function, decoding how it shifts and changes is crucial. But it’s very hard to do, even in labs.

“It’s like you’re riding a bicycle in a windstorm,” Bhattacharya said. “You’re constantly being pushed away from your path.”

But team members are on their way toward predicting it using AI. They demonstrated some of the models recently in the lab. Tarafder brought up a large RNA structure that contorted itself into various shapes on the screen.

“A single molecule can have thousands of atoms, and predicting the exact position of each one is extremely difficult,” he said. “But if you can accurately predict these 3D structures, you can find druggable pockets — places where you can target treatments. That’s how you start to develop solutions for different diseases.”

The National Institutes of Health award is a big step forward for this work, and with federal support, Bhattacharya said he will be able to add more junior researchers to his team soon. But he’s happy with the promising work so far.

“I feel very lucky to work with these students. This is a collective effort,” Bhattacharya said. “They have worked immensely hard, and they deserve the spotlight.”

The admiration goes both ways.

“We’re really proud of our advisor,” Wang said. “This kind of funding makes our work possible — without it, we couldn’t do this research.”