Abstract: The discovery of biomolecules with desired properties is critical to advances in drug discovery and synthetic biology. This problem is challenging due to the combinatorial search space of biomolecules. In this talk, I will present how generative AI can be used to accelerate the discovery process across small molecules, proteins, and RNAs. First, I will present a generative deep learning approach for de novo antibiotic design, where AI methods successfully discovered two lead compounds with in vivo bactericidal efficacy against multidrug-resistant bacteria in mice models. Second, I will present an energy-based modeling approach for protein design named BindEnergyCraft, which provides a principled way for calculating the likelihood of 3D structures and substantially improves the in silico binder success rate of current state-of-the-art binder design methods. Lastly, I will present a diffusion model-based approach for designing RNA translational control elements, using internal ribosome entry sites (IRESs) as a model system. Validated in human cells, we find that AI-generated IRESs circumvent natural sequence constraints and improve IRES activity by nearly 10 fold. In summary, our in silico and experimental results highlight the potential of generative AI for accelerating biomolecular design.

 

 

Short Bio: Wengong Jin is an assistant professor at Khoury College of Computer Sciences at Northeastern University and a visiting research scientist in the Eric and Wendy Schmidt Center at Broad Institute. His research focuses on geometric and generative AI models for drug discovery and synthetic biology. His work has been published in journals including ICML, NeurIPS, ICLR, Nature, Science, Cell, and PNAS, and covered by such outlets as the Guardian, BBC News, CBS Boston, and the Financial Times. He is the recipient of the Google Research Scholar Award, BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award.