Bayesian optimization (BayesOpt) is a gold standard for query-efficient
continuous optimization. However, its adoption for drug 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, allowing 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 tradeoff over multiple design rounds, and to balance objective
tradeoffs by optimizing sequences at many different points on the Pareto
frontier. We evaluate LaMBO on two small-molecule design tasks, and introduce
new tasks optimizing \emph{in silico} and \emph{in vitro} properties of
large-molecule fluorescent proteins. In our experiments LaMBO outperforms
genetic optimizers and does not require a large pretraining corpus,
demonstrating that BayesOpt is practical and effective for biological sequence
design.

LaMBO 是一种新的 Bayesian optimization 方法，采用了联合训练的方法，结合 Denoising Autoencoder 和多任务 Gaussian Process Head 进行多目标优化，以实现在离散高维空间中进行生物序列设计。