期刊
ANALYTICAL CHEMISTRY
卷 92, 期 20, 页码 13971-13979出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.0c02878
关键词
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资金
- National Key R&D Program of China [2016YFA0203103]
- National Natural Science Foundation of China [91543204, 91643204]
- introduced innovative R&D team project under The Peal River Talent Recruitment Program of Guangzhou Province [2019ZT08L387]
Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This end-to-end deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.
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