Title: Generative AI for Antibiotic Discovery and Synthetic Biology

Abstract: In this talk, we highlight how we are using generative AI for antibiotic discovery and synthetic biology. In the first part of the talk, we focus on the Antibiotics-AI Project, which is a multi-disciplinary, innovative research program that is leveraging MIT's strengths in artificial intelligence, bioengineering, and the life sciences to discover and design novel classes of antibiotics. The Antibiotics-AI Project is focused on developing, integrating and implementing deep learning models and chemogenomic screening approaches: (1) to predict novel antibiotics from expansive chemical libraries with diverse properties, (2) to design de novo novel antibiotics based on learned structural and functional properties of existing and newly discovered antibiotics, and (3) to identify, using explainable deep learning models, the chemical structures and molecular mechanisms underlying the newly discovered and/or designed antibiotics.Our platform has been designed so that it can be utilized and applied in a rapid fashion to emerging and re-emerging bacterial pathogens, including multidrug-resistant (MDR) bacteria and extensively drug-resistant (XDR) bacteria. In the second part of the talk, we focus on synthetic biology, which is an emerging field that is bringing together engineers, computer scientists and biologists to model, design and construct biological circuits out of proteins, genes and other bits of DNA, and to use these circuits to rewire and reprogram organisms. These re-engineered organisms are going to change our lives in the coming years, leading to cheaper drugs, rapid diagnostic tests, and synthetic probiotics to treat infections and a range of complex diseases. We highlight recent efforts that use generative AI in the context of synthetic biology to create novel classes of diagnostics, therapeutics, and programmable molecular tools for the life sciences.