Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
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DOI：
10.48550/arxiv.2203.12742
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
S. Stanton;Wesley J. Maddox;Nate Gruver;Phillip M. Maffettone;E. Delaney;Peyton Greenside;A. Wilson-A.-Wils
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文献类型：
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作者：
S. Stanton;Wesley J. Maddox;Nate Gruver;Phillip M. Maffettone;E. Delaney;Peyton Greenside;A. Wilson-A.-Wils
贝叶斯优化(BayesOpt)是查询高效的连续优化的黄金标准。然而，决策变量的离散、高维性质阻碍了它在药物设计中的采用。我们开发了一种新的方法(LAMBO)，它联合训练一个带有鉴别多任务高斯过程头部的去噪自动编码器，允许在自动编码器的潜在空间中基于梯度的多目标捕获函数的优化。这些获取功能使兰博能够在多个设计回合中平衡探索和利用之间的权衡，并通过在帕累托前沿的许多不同点优化序列来平衡目标权衡。我们在两个小分子设计任务中对Lambo进行了评估，并引入了新的任务来优化大分子荧光蛋白的性质。在我们的实验中，Lambo的性能优于遗传优化器，并且不需要大量的预训练语料库，证明了BayesOpt对于生物序列设计是实用和有效的。
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.