将学习从定制的虚拟分子数据库转移到现实世界的有机光敏剂催化活性预测。

IF 6.2 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Naoki Noto, Taiki Nagano, Mikito Fujinami, Ryosuke Kojima, Susumu Saito
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引用次数: 0

摘要

实验训练数据的缺乏限制了机器学习在催化研究中的整合。在这里,我们报告了在分子拓扑指数上预训练的图卷积网络(GCN)模型的有效性,该模型不用于典型的有机合成,用于估计催化活性,这一任务通常需要高水平的人类专业知识。对于预训练,我们使用定制的虚拟分子数据库,可以使用系统生成方法或我们小组开发的分子生成器轻松构建。尽管94%-99%使用的虚拟分子未在PubChem数据库中注册,但由此产生的预训练GCN模型提高了对真实有机光敏剂催化活性的预测。结果证明了当前迁移学习策略的有效性,该策略利用了自生成的虚拟分子中容易获得的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.

The scarcity of experimental training data restricts the integration of machine learning into catalysis research. Here, we report on the effectiveness of graph convolutional network (GCN) models pretrained on a molecular topological index, which is not used in typical organic synthesis, for estimating the catalytic activity, a task that usually requires high levels of human expertise. For pretraining, we used custom-tailored virtual molecular databases that can be readily constructed using either a systematic generation method or a molecular generator developed in our group. Although 94%-99% of the employed virtual molecules are unregistered in the PubChem database, the resulting pretrained GCN models improve the prediction of catalytic activity for real-world organic photosensitizers. The results demonstrate the efficiency of the present transfer-learning strategy, which leverages readily obtainable information from self-generated virtual molecules.

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来源期刊
Communications Chemistry
Communications Chemistry Chemistry-General Chemistry
CiteScore
7.70
自引率
1.70%
发文量
146
审稿时长
13 weeks
期刊介绍: Communications Chemistry is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the chemical sciences. Research papers published by the journal represent significant advances bringing new chemical insight to a specialized area of research. We also aim to provide a community forum for issues of importance to all chemists, regardless of sub-discipline.
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