3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs
Professor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.
Professor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.
Assistant Professor Yunha Hwang utilizes microbial genomes to examine the language of biology. Her appointment reflects MIT’s commitment to exploring the intersection of genetics research and AI.
MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
MIT Sea Grant students apply machine learning to support local aquaculture hatcheries.
Most antibiotics target metabolically active bacteria, but with artificial intelligence, researchers can efficiently screen compounds that are lethal to dormant microbes.
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.
With further development, the programmable system could be used in a range of applications including gene and cancer therapies.