论文标题
加速贝叶斯对生物序列设计的优化，并使用denoising自动编码器
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
论文作者
论文摘要
贝叶斯优化（Bayesopt）是查询有效连续优化的金标准。但是，决策变量的离散，高维性质阻碍了其对药物设计的采用。我们开发了一种新的方法（LAMBO），该方法通过判别性多任务高斯流程主管共同训练Denoising AutoCododer，从而使基于梯度的多目标采集功能优化了自动装饰器的潜在空间。这些采集功能使Lambo能够在多个设计回合上平衡探索探索折衷方案，并通过在Pareto Frontier上的许多不同点上优化序列来平衡客观权衡。我们在两个小分子设计任务上评估了兰博，并引入了优化\ emph {in silico}和\ emph {Inter {Inter}的特性的新任务。在我们的实验中，兰博的表现优于遗传优化器，不需要大量的训练训练库，这表明贝叶诺斯对生物序列设计是实用且有效的。
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.
