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Protein generation and fitness optimization

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Deep learning is rapidly advancing the best computational tools in computational biology. In this talk, I will discuss recent advancements in using generative models and sampling algorithms to generate proteins and optimize their fitness. First, I will go over FrameDiff in which we lay the mathematical foundation of SE(3) diffusion and introduce a practical algorithm for training a frame-based generative model over protein backbones. SE(3) diffusion is then utilized in a state-of-the-art protein design method, RFdiffusion, that is pre-trained on protein structure prediction. To conclude, I will go over latest work in sequence-based protein fitness optimization using Gibbs with Gradients. We argue for the importance of model regularization for handling protein fitness sparsity and ensuring a smooth optimization landscape.

Bio: Jason Yim is a second year EECS PhD student at MIT advised by Tommi Jaakkola and Regina Barzilay. Previously, he graduated from Johns Hopkins University with a bachelors in computer science then worked at DeepMind at a research engineer on medical imaging and AlphaFold-multimer. His research interest is to develop machine learning methods with applications to scientific problems. His latest works focus on inverse problems in protein design.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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