用变分方法高效生成稀有设计，提升生物序列工程效率
Variational Search Distributions
- 基于变分推断构建条件生成模型，批量顺序优化黑箱评估
- 在蛋白质与核酸设计任务中显著优于现有基线方法
- 可适配大规模预测模型，适合生物分子设计场景
我们提出VSD方法，通过高效批量顺序评估黑箱（如实验或模拟），在稀有目标类别上对离散组合设计的生成模型进行条件化。该任务称为主动生成，我们对其需求和期望进行了形式化，并通过变分推断提供解决方案。VSD采用现成的基于梯度的优化算法，可学习强大的理想设计生成模型，并利用可扩展的预测模型。我们推导了在特定配置下学习真实条件生成分布的渐近收敛速率。在图像生成示例后，我们实证表明，VSD在多个真实的序列设计问题中，包括蛋白质与DNA/RNA工程任务，均优于现有基线方法。
原文摘要 · Abstract (English)
We develop VSD, a method for conditioning a generative model of discrete, combinatorial designs on a rare desired class by efficiently evaluating a black-box (e.g. experiment, simulation) in a batch sequential manner. We call this task active generation; we formalize active generation's requirements and desiderata, and formulate a solution via variational inference. VSD uses off-the-shelf gradient based optimization routines, can learn powerful generative models for desirable designs, and can take advantage of scalable predictive models. We derive asymptotic convergence rates for learning the true conditional generative distribution of designs with certain configurations of our method. After illustrating the generative model on images, we empirically demonstrate that VSD can outperform existing baseline methods on a set of real sequence-design problems in various protein and DNA/RNA engineering tasks.
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