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Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol.
The Journal of Chemical Physics ( IF 4.4 ) Pub Date : 2020-09-18 , DOI: 10.1063/5.0013278
Cecilia Muraro 1 , Mirko Polato 2 , Marco Bortoli 1 , Fabio Aiolli 2 , Laura Orian 1
Affiliation  

Many natural substances and drugs are radical scavengers that prevent the oxidative damage to fundamental cell components. This process may occur via different mechanisms, among which, one of the most important, is hydrogen atom transfer. The feasibility of this process can be assessed in silico using quantum mechanics to compute ΔGHAT. This approach is accurate, but time consuming. The use of machine learning (ML) allows us to reduce tremendously the computational cost of the assessment of the scavenging properties of a potential antioxidant, almost without affecting the quality of the results. However, in many ML implementations, the description of the relevant features of a molecule in a machine-friendly language is still the most challenging aspect. In this work, we present a newly developed machine-readable molecular representation aimed at the application of automatized ML algorithms. In particular, we show an application on the calculation of ΔGHAT.

中文翻译:

天然抗氧化剂和药物的自由基清除活性:结合机器学习和量子化学方案的开发。

许多天然物质和药物都是自由基清除剂,可防止对基本细胞成分的氧化损伤。该过程可以通过不同的机制发生,其中最重要的机制之一是氢原子转移。可以使用量子力学在计算机上评估此过程的可行性,以进行计算ΔG帽子。这种方法是准确的,但是很耗时。机器学习(ML)的使用使我们可以大大减少评估潜在抗氧化剂清除特性的评估的计算成本,而几乎不会影响结果的质量。但是,在许多ML实现中,以机器友好的语言描述分子的相关特征仍然是最具挑战性的方面。在这项工作中,我们提出了针对自动ML算法应用的最新开发的机器可读分子表示。特别是,我们展示了在计算ΔG帽子
更新日期:2020-09-21
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