Building AI models that understand chemical principles
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.
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.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
MIT engineers used a machine-learning model to design nanoparticles that can deliver RNA to cells more efficiently.
ChemXploreML makes advanced chemical predictions easier and faster — without requiring deep programming skills.
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.
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Junior Katie Spivakovsky describes her path through New Engineering Education Transformation to biomedical research and beyond.
Most antibiotics target metabolically active bacteria, but with artificial intelligence, researchers can efficiently screen compounds that are lethal to dormant microbes.