Representation of molecular structures with persistent homology for machine learning applications in chemistry
Representation of molecular structures with persistent homology for machine learning applications in chemistry
复制标题
DOI：
10.1038/s41467-020-17035-5
复制
发表时间：
2020-06-26
影响因子：
16.6
通讯作者：
Vogiatzis, Konstantinos D.
中科院分区：
文献类型：
--
作者：
Townsend, Jacob;Micucci, Cassie Putman;Vogiatzis, Konstantinos D.
机器学习和高通量计算筛选已经成为加速第一性原理筛选的有价值的工具，用于发现下一代功能化分子和材料。机器学习在化学应用中的应用需要将分子结构转换为称为分子表示的机器可读格式。这种表示的选择影响化学机器学习方法的性能和结果。在这里，我们提出了一个新的简洁的分子表示来自持久的同源性，数学的应用分支。我们已经证明了它的适用性，在一个大的分子数据库（GDB-9）的高通量计算筛选超过133，000个有机分子。我们的目标是确定选择性地与CO2相互作用的新分子。提出了新的分子指纹方法的方法和性能，并使用新的化学驱动的持久性图像表示来筛选GDB-9数据库，以建议具有增强特性的分子和/或官能团。分子表示的选择会严重影响机器学习方法的性能。在这里，作者展示了一个持久性的同源性为基础的分子表示，通过主动学习的方法来预测CO2/N-2的相互作用能在密度泛函理论（DFT）水平。
Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties. The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N-2 interaction energies at the density functional theory (DFT) level.