用CRPS评分优化实验设计，精准找特定性质分子
CRPS-Based Targeted Sequential Design with Application in Chemical Space
- 以修正的连续排名概率得分（CRPS）为依据设计实验
- 在化学空间中更高效找到目标性质的分子
- 适合需要精准筛选的药物与材料研发
通过高斯过程（GP）模型进行序列实验设计，已证明在数据采集稀疏、目标导向的场景中极具价值。本文聚焦于响应值在预设范围内的建模精度需求，适用于合成化学等领域的分子筛选——发现具有特定性质的分子对新材料和药物开发至关重要。我们提出将阈值加权的连续排名概率得分（CRPS）作为序列设计中获取函数的基础构建模块，研究了基于点态与积分形式的两种加权准则，并与现有方法对比，验证了其在目标达成上的优越性。该策略可广泛应用于多领域，为基于评分规则的序列设计发展奠定基础。
原文摘要 · Abstract (English)
Sequential design of real and computer experiments via Gaussian Process (GP) models has proven useful for parsimonious, goal-oriented data acquisition purposes. In this work, we focus on acquisition strategies for a GP model that needs to be accurate within a predefined range of the response of interest. Such an approach is useful in various fields including synthetic chemistry, where finding molecules with particular properties is essential for developing useful materials and effective medications. GP modeling and sequential design of experiments have been successfully applied to a plethora of domains, including molecule research. Our main contribution here is to use the threshold-weighted Continuous Ranked Probability Score (CRPS) as a basic building block for acquisition functions employed within sequential design. We study pointwise and integral criteria relying on two different weighting measures and benchmark them against competitors, demonstrating improved performance with respect to considered goals. The resulting acquisition strategies are applicable to a wide range of fields and pave the way to further developing sequential design relying on scoring rules.
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