Related papers: Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

URL: http://arxiv.org/abs/2203.12742v1
Date: Wed, 23 Mar 2022 21:58:45 GMT
Title: Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Authors: Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Abstract summary: We develop a new approach which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head. We evaluate LaMBO on a small-molecule based on the ZINC dataset and introduce a new large-molecule task targeting fluorescent proteins.
Score: 28.550684606186884
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Bayesian optimization is a gold standard for query-efficient continuous optimization. However, its adoption for drug and antibody sequence design has been hindered by the discrete, high-dimensional nature of the decision variables. We develop a new approach (LaMBO) which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head, enabling gradient-based optimization of multi-objective acquisition functions in the latent space of the autoencoder. These acquisition functions allow LaMBO to balance the explore-exploit trade-off over multiple design rounds, and to balance objective tradeoffs by optimizing sequences at many different points on the Pareto frontier. We evaluate LaMBO on a small-molecule task based on the ZINC dataset and introduce a new large-molecule task targeting fluorescent proteins. In our experiments, LaMBO outperforms genetic optimizers and does not require a large pretraining corpus, demonstrating that Bayesian optimization is practical and effective for biological sequence design.