Mottafegh Amirreza, Ahn Gwang-Noh, Kim Dong-Pyo
Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
Lab Chip. 2023 Mar 14;23(6):1613-1621. doi: 10.1039/d2lc00938b.
Optimizing a wide range of reaction parameters, steps, and pathways is currently considered one of the most complex and challenging problems in microflow-based organic synthesis. As a novel solution, Bayesian optimization (BO) has been utilized to efficiently guide the optimized conditions of flow reactors; however, the benchmarking process for selecting the optimal model among various surrogate models remains inefficient. In this work, we report meta optimization (MO) by benchmarking multiple surrogate models in real-time without any pre-work, which is realized by evaluating the expected values obtained by the regressor used to build each surrogate model, enabling efficient optimization of reaction conditions. By the comparison of the performance of MO with that of various BOs on four datasets of different flow syntheses, it was verified that MO consistently performs the best-in-class for all emulators developed through machine learning, while the conventional BOs based on surrogate models such as the Gaussian process, random forest, neural network ensemble, and gradient boosting demonstrated varying performances from each emulator, which implies that benchmarking is required.
优化广泛的反应参数、步骤和途径目前被认为是基于微流控的有机合成中最复杂和最具挑战性的问题之一。作为一种新颖的解决方案，贝叶斯优化（BO）已被用于有效指导流动反应器的优化条件；然而，在各种替代模型中选择最优模型的基准测试过程仍然效率低下。在这项工作中，我们报告了一种元优化（MO）方法，即无需任何前期工作即可实时对多个替代模型进行基准测试，这是通过评估用于构建每个替代模型的回归器获得的期望值来实现的，从而能够高效地优化反应条件。通过在四个不同流动合成数据集上比较MO与各种BO的性能，验证了MO在通过机器学习开发的所有模拟器中始终表现最佳，而基于高斯过程、随机森林、神经网络集成和梯度提升等替代模型的传统BO在每个模拟器上表现出不同的性能，这意味着需要进行基准测试。
