摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-一项关于人工智能的新研究现在可用。根据《新闻周刊》编辑在釜山国立大学的新闻报道，研究表明：“立体选择性反应在生命、进化、人类生物学和医学的出现中发挥了重要作用。”这项研究的资金支持者包括国家科学基金会。我们的新闻记者从釜山国立大学获得了一句话：“然而，在很长一段时间里，”在立体选择性反应中,许多工业和学术研究都是采用反复试验的方法进行不对称合成,另外,以往的研究大多集中在空间效应和电子效应对立体选择性反应的影响上,因此,要全面了解某一化学反应的立体选择性是非常困难的。本文提出了一种新的组合机器学习方法,用于定量预测反应中一种对映体优先生成的程度,具体地说,机器学习方法广泛应用于数据分析中,包括随机森林法、支持向量回归法、LASSO法等。为深入理解反应和准确预测离子提供了贝叶斯优化和置换重要性定理。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting out of Pusan National University by NewsRx editors, research stated, "Stereoselective reactions have played a vit al role in the emergence of life, evolution, human biology, and medicine."Financial supporters for this research include National Science Foundation. Our news reporters obtained a quote from the research from Pusan National Univer sity: "However, for a long time, most industrial and academic efforts followed a trial-and-error approach for asymmetric synthesis in stereoselective reactions. In addition, most previous studies have been qualitatively focused on the influ ence of steric and electronic effects on stereoselective reactions. Therefore, q uantitatively understanding the stereoselectivity of a given chemical reaction i s extremely difficult. As proof of principle, this paper develops a novel compos ite machine learning method for quantitatively predicting the enantioselectivity representing the degree to which one enantiomer is preferentially produced from the reactions. Specifically, machine learning methods that are widely used in d ata analytics, including Random Forest, Support Vector Regression, and LASSO, ar e utilized. In addition, the Bayesian optimization and permutation importance te sts are provided for an in-depth understanding of reactions and accurate predict ion."
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