BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
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.
Read MoreVaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
Read MoreSolubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
Read MoreThe team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
Read MoreStarting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Read MoreWith models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Read MoreThe program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
Read MoreThe SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
Read More
You must be logged in to post a comment.