论文标题
部分可观测时空混沌系统的无模型预测
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
论文作者
论文摘要
深层生成模型已成为一种流行的基于机器学习的方法，用于生命科学中的逆设计问题。但是，除了学习数据分布之外，这些问题通常需要采样新设计，以满足感兴趣的多个属性。当属性彼此独立或正交时，这种多目标优化变得更具挑战性。在这项工作中，我们提出了一个基于帕累托 - 复合能量模型（PCEBM），该模型（PCEBM）使用多个梯度下降来对新设计进行采样，以遵守各种约束，以优化不同的特性。我们展示了其学习非凸帕雷托前沿的能力，并生成序列，这些序列同时满足了一系列现实世界中的抗体设计任务的多个所需属性。
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution. This multi-objective optimization becomes more challenging when properties are independent or orthogonal to each other. In this work, we propose a Pareto-compositional energy-based model (pcEBM), a framework that uses multiple gradient descent for sampling new designs that adhere to various constraints in optimizing distinct properties. We demonstrate its ability to learn non-convex Pareto fronts and generate sequences that simultaneously satisfy multiple desired properties across a series of real-world antibody design tasks.
