[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Automatic Drawing of Orbital Correlation Diagrams. A Computational Tool for Electronic-Structure Informatics

M. Sugimoto, Takafumi Inoue
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Abstract

Finding direct correlations between electronic structures of molecules and their properties, which we call “electronic-structure informatics”, is one of the challenging issues in chemoinformatics because the electronic degree of freedom is an essential factor determining the chemical characteristics. Herein we develop computational methods to automatically draw two types of orbital correlation diagrams. They are expected useful to perform machine learning including electronic degrees of freedom. In the present approach, we focus on electronic similarity called orbital similarity whose score is defined as spatial overlap between two molecular orbitals (MOs) enclosed with their iso-value surfaces. The similarity scores are also used to derive another orbital correlation diagram called “orbital interaction diagram”. This diagram is to relate MOs of a target molecule with those of its fragments. Through applications to benzene derivatives, these diagrams are shown to be reasonable, indicating potential usefulness of the present method in machine learning for quantitative predictions of molecular properties and chemical reactivities.
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[船松木东教授荣誉奖特刊]轨道相关图的自动绘制。电子结构信息学的计算工具
寻找分子的电子结构与其性质之间的直接关系,我们称之为“电子结构信息学”,是化学信息学中具有挑战性的问题之一,因为电子自由度是决定化学特性的重要因素。本文提出了自动绘制两种轨道相关图的计算方法。它们有望用于执行包括电子自由度在内的机器学习。在目前的方法中,我们关注的是称为轨道相似性的电子相似性,其分数被定义为两个分子轨道(MOs)之间的空间重叠,它们的等值表面被包围。相似度分数还用于推导另一种称为“轨道相互作用图”的轨道相关图。这张图将目标分子的MOs与其片段的MOs联系起来。通过对苯衍生物的应用,这些图被证明是合理的,表明本方法在机器学习中对分子性质和化学反应性的定量预测的潜在有用性。
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来源期刊
Journal of Computer Aided Chemistry
Journal of Computer Aided Chemistry CHEMISTRY, MULTIDISCIPLINARY-
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