Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization

2026-09-16

AI Summary

本文解决了虚拟筛选中贝叶斯优化计算开销大的问题，通过利用球形域上的线性模型加速了决策过程，提高了分子和图像生成基准性能。

Abstract

Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of candidates to experimentally validate. While Bayesian optimization (BO) is a natural fit for this setting, as it uses past evaluations to guide future proposals, the computational overhead required for its sequential decision-making becomes a bottleneck when virtual screens are relatively cheap. We make BO practical in this regime by exploiting the unique combination of a linear model constrained to a spherical domain where high-dimensional latents concentrate. We build off recent work justifying the use of linear surrogates, while deriving nearly closed-form solutions to the surrogate modelling and acquisition problems that exploit spherical symmetry. The result is at least a 100x speedup over state-of-the art baselines, with matching or improved performance across molecular and image generation benchmarks. Altogether, our method makes BO a practical drop-in for de novo pipelines where it was previously too slow to consider.

Problem

• Bayesian Optimization
• Generative Models
• Virtual Screening
• Latent Space

Innovation

• Bayesian Optimization
• Linear Surrogates
• Spherical Symmetry
• Generative Models