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Improving molecular design through a machine learning approach
Chemical Engineering and Processing: Process Intensification ( IF 4.3 ) Pub Date : 2020-10-17 , DOI: 10.1016/j.cep.2020.108173
Darinel Valencia-Marquez , Antonio Flores-Tlacuahuac

We present a study for predicting 15 molecular properties through the combination of a quantum mechanical database, taken from the quantum chemistry QM9 database, and feed forward deep neural networks approaches. The aim of the work is to show that the combination of a priori computed ab-initio information and machine learning can support experimental work to speed up the discovery and formulation of novel compounds. The importance of this work also relies on the fact that through this computer-aided molecular design approach no approximate or heuristic contribution methods are needed for physical and thermodynamic properties information. We show that using proper hyper-parameters tuning of deep neural networks is possible, even with modest computational resources, to design the chemical structure of compounds matching target molecular properties making them feasible for practical industrial applications in diverse areas such as energy, water, food, health and transport economical sectors.



中文翻译:

通过机器学习方法改善分子设计

我们提出了一项通过结合量子力学数据库(从量子化学QM9数据库中提取)来预测15种分子性质的研究,并提出了深层神经网络方法。这项工作的目的是表明,先验计算的从头算信息和机器学习的结合可以支持实验工作,以加快新型化合物的发现和形成。这项工作的重要性还取决于以下事实:通过这种计算机辅助分子设计方法,不需要物理或热力学性质信息的近似或启发式贡献方法。我们证明即使使用适度的计算资源,也可以使用适当的超参数对深度神经网络进行调整,

更新日期:2020-10-30
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