Every atom counts: predicting sites of reaction based on chemistry within two bonds†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Ching Ching Lam and Jonathan M. Goodman
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引用次数: 0

Abstract

How much chemistry can be described by looking only at each atom, its neighbours and its next-nearest neighbours? We present a method for predicting reaction sites based only on a simple, two-bond model. Machine learning classification models were trained and evaluated using atom-level labels and descriptors, including bond strength and connectivity. Despite limitations in covering only local chemical environments, the models achieved over 80% accuracy even with challenging datasets that cover a diverse chemical space. Whilst this simplistic model is necessarily incomplete, it describes a large amount of interesting chemistry.

Abstract Image

每个原子都很重要:根据两个化学键内的化学反应预测反应场所†。
只看每个原子、其邻原子和近邻原子,能描述多少化学反应?我们介绍了一种仅基于简单双键模型预测反应场所的方法。我们使用原子级标签和描述符(包括键强度和连通性)对机器学习分类模型进行了训练和评估。尽管存在仅覆盖局部化学环境的局限性,但这些模型的准确率达到了 80% 以上,即使是在覆盖多种化学空间的挑战性数据集上也是如此。虽然这种简单化的模型必然是不完整的,但它描述了大量有趣的化学现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.80
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0.00%
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