CatScore: evaluating asymmetric catalyst design at high efficiency

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-07-11 DOI:10.1039/D4DD00114A
Bing Yan and Kyunghyun Cho
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Abstract

Asymmetric catalysis plays a crucial role in advancing medicine and materials science. However, the prevailing experiment-driven methods for catalyst evaluation are both resource-heavy and time-consuming. To address this challenge, we present CatScore – a learning-centric metric designed for the automatic evaluation of catalyst design models at both instance and system levels. This approach harnesses the power of deep learning to predict product selectivity as a function of reactants and the proposed catalyst. The predicted selectivity serves as a quantitative score, enabling a swift and precise assessment of a catalyst's activity. On an instance level, CatScore's predictions correlate closely with experimental outcomes, demonstrating a Spearman's ρ = 0.84, which surpasses the density functional theory (DFT) based linear free energy relationships (LFERs) metric with ρ = 0.55 and round-trip accuracy metrics at ρ = 0.24. Importantly, when ranking catalyst candidates, CatScore achieves a mean reciprocal ranking significantly superior to traditional LFER methods, marking a considerable reduction in labor and time investments needed to find top-performing catalysts.

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CatScore:评估高效不对称催化剂设计
不对称催化在推动医学和材料科学发展方面发挥着至关重要的作用。然而,目前用于催化剂评估的实验驱动方法既耗费资源又耗费时间。为了应对这一挑战,我们提出了 CatScore--一种以学习为中心的度量方法,设计用于在实例和系统层面自动评估催化剂设计模型。这种方法利用深度学习的强大功能,将产物选择性作为反应物和拟议催化剂的函数进行预测。预测的选择性可作为量化评分,从而对催化剂的活性进行快速、精确的评估。在实例层面上,CatScore 的预测与实验结果密切相关,显示出 Spearman's ρ = 0.84,超过了密度泛函理论(DFT)的 ρ = 0.54 和往返精度指标 ρ = 0.24。重要的是,在对候选催化剂进行排名时,CatScore 的平均倒数排名明显优于传统的 DFT 方法,大大减少了寻找性能最佳催化剂所需的人力和时间投入。
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