Events

CILVR Seminar: Foundations behind the Prescient Design’s approach to protein design

Speaker: Kyunghyun Cho

Location: On-Line
Videoconference link: https://nyu.zoom.us/j/97986595706

Date: Tuesday, February 8, 2022

Prescient Design at Genentech investigates, develops and deploys a machine learning driven platform for protein design. At the core of Prescient Design’s strategy is the deep manifold sampler which is a non-autoregressive sequence denoising autoencoder combined with a function predictor. In this talk, I will describe the foundations behind the deep manifold sampler. In the first part, I will discuss the background on generative modeling in depth, starting from energy-based modeling, restricted Boltzmann machines to denoising autoencoders. This will be followed by the second part in which sampling from a discriminative model, such as a classifier, will be discussed carefully, particularly focusing on their shortcomings. In the final part of the talk, I will talk about how these two paradigms can be combined to complement each other and present the deep manifold sampler as a representative example specifically in the context of sequence-level protein design.