物理模型的贝叶斯预测及其在化学动力学合成药物中的应用
Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics
摘要：
工业过程中的质量控制越来越多地利用先前的科学知识，这些知识通常编码在需要数值近似的物理模型中。统计预测和后续优化是确保过程输出满足规格目标的关键。然而，近似的模型的数值费用提出了计算的挑战，识别的组合的过程中的因素，有信心的质量的响应。最近的工作在贝叶斯计算和统计近似（仿真）昂贵的计算模型被用来开发一种新的策略，优化后验概率的过程满足规格。随后的方法是由化学合成工艺驱动的，并在该工艺中得到证明，该工艺用于生产药品，其中一组初始物质在某些工艺条件下根据化学反应演变成一系列新物质。其中一种物质是目标药物产品，两种是不需要的副产品。目的是确定工艺条件和初始物质量的组合，以最大限度地提高获得足够目标药物产品的可能性，同时确保不需要的副产物不超过给定水平。的因素和感兴趣的物质的量之间的关系在理论上描述的解决方案的常微分方程系统，包括温度依赖性。使用数据从一个小的实验，它显示了如何的方法可以近似的多变量后验预测分布的药物目标和副产品，因此确定合适的操作值。重复分析的材料可在www.github.com/amo105/chemicalkinetics上找到。
英文摘要：
Quality control in industrial processes is increasingly making use of prior scientific knowledge, often encoded in physical models that require numerical approximation. Statistical prediction, and subsequent optimization, is key to ensuring the process output meets a specification target. However, the numerical expense of approximating the models poses computational challenges to the identification of combinations of the process factors where there is confidence in the quality of the response. Recent work in Bayesian computation and statistical approximation (emulation) of expensive computational models is exploited to develop a novel strategy for optimizing the posterior probability of a process meeting specification. The ensuing methodology is motivated by, and demonstrated on, a chemical synthesis process to manufacture a pharmaceutical product, within which an initial set of substances evolve according to chemical reactions, under certain process conditions, into a series of new substances. One of these substances is a target pharmaceutical product and two are unwanted by-products. The aim is to determine the combinations of process conditions and amounts of initial substances that maximize the probability of obtaining sufficient target pharmaceutical product whilst ensuring unwanted by-products do not exceed a given level. The relationship between the factors and amounts of substances of interest is theoretically described by the solution to a system of ordinary differential equations incorporating temperature dependence. Using data from a small experiment, it is shown how the methodology can approximate the multivariate posterior predictive distribution of the pharmaceutical target and by-products, and therefore identify suitable operating values. Materials to replicate the analysis can be found at www.github.com/amo105/chemicalkinetics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
