A new model predicts how molecules will dissolve in different solvents
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
The Substance Use Disorders Ventures Bootcamp ignites innovators like Evan Kharasch to turn research breakthroughs into treatments for substance use disorder.
Protein sensor developed by alumna-founded Advanced Silicon Group can be used for research and quality control in biomanufacturing.
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
A new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.
Starting 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.
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Novel method to scale phenotypic drug screening drastically reduces the number of input samples, costs, and labor required to execute a screen.
Inspired by traditional medicine, 17-year-old Tomás Orellana is on a mission to identify plants that can help treat students’ health issues.
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
Hanna Adeyema and Carolina Haass-Koffler discuss the substance use disorder crisis and the future of innovation in the field.
Large multi-ring-containing molecules known as oligocyclotryptamines have never been produced in the lab until now.
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.