Pranam Chatterjee Designs Novel AI Frameworks for Biotechnology

Summary

Pranam Chatterjee Designs Novel AI Frameworks for Biotechnology In the News / September 18, 2025 When he enrolled at Dartmouth, Pranam Chatterjee, Assistant Professor and Africk-Lesley Distinguished Scholar of Innovation in Engineering with a joint appointment in Bioengineering (BE) and in Computer and Information Science (CIS), did not start off as an engineering student. He majored in religion. “I had a deep desire to understand the people around me,” says Chatterjee, who grew up in a household of scientists on the Alabama-Georgia border. But after transferring to MIT, where he returned to his science roots and studied biology and computer science, Chatterjee brought the unique perspective of the humanities with him. “Studying religion taught me to be a people-first engineer,” he says. “No matter how powerful your technology is, if it doesn’t fit into the way people see the world, it won’t have the impact you want.” From the Humanities to AI for Biotech Chatterjee quickly turned to artificial intelligence (AI) as his tool of choice in the engineering world, asking himself the question,”How can AI be used first and foremost to solve the most pressing problems facing humanity?” The pursuit of the answer to this question cleared a path that would lead him to the forefront of AI for biotechnology. “Biotechnology has the potential to solve so many of the world’s most pressing problems, from health and medicine to energy, agriculture, food security and the environment,” Chatterjee says. “When designed well, AI can make sense of biological complexity and design solutions at scale.” Today, Chatterjee is a member of the Center for Precision Engineering for Health, where his lab develops generative AI models to identify and design molecular therapeutics that have the potential to treat diseases considered “undruggable,” like pediatric cancers, which are caused by unstable, malfunctioning proteins. These diseases are hard to target and synthesize, making the endeavor challenging. But Chatterjee saw the problem and turned it into an opportunity that AI could help solve. Generative AI for Biology’s Edge Cases In his Penn Engineering lab, the workflow is strikingly direct. Publicly available biological data — often from patient samples or past clinical trials — feeds into advanced generative algorithms. These models don’t just predict properties of molecules; they crea…