Organic fluorescent small molecule optical property prediction method based on deep neural network

A technology of deep neural network and optical properties, which is applied in the field of prediction of optical properties of organic fluorescent small molecules, can solve the problems of time cost and resource investment, low success rate, etc.

Pending Publication Date: 2021-09-10
ZHEJIANG UNIV
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Problems solved by technology

At present, the development of most new organic fluorescent molecules is still based on synthesizing a series of compounds at the same time, so as to examine their optical ...

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  • Organic fluorescent small molecule optical property prediction method based on deep neural network
  • Organic fluorescent small molecule optical property prediction method based on deep neural network
  • Organic fluorescent small molecule optical property prediction method based on deep neural network

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Embodiment Construction

[0018] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0019] see Figure 1-Figure 5 , a method for predicting the optical properties of organic fluorescent small molecules based on a deep neural network according to an embodiment of the present invention, which includes the following steps:

[0020] Step S100: Obtain organic fluorescent small molecule data from published literature, arrange and classify, specifically include the following steps: Step S101: Search literature by searching keywords and structural formulas, collect and organize information on organic fluorescent small molecule structures and maximum absorption wavelengths literature with clear reports.

[0021] Step S102: Collect the data of organic fluoresce...

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Abstract

The invention provides an organic fluorescent small molecule optical property prediction method based on a deep neural network. According to the method, a new organic fluorescent small molecule database is established, molecular descriptors and molecular fingerprints are adopted to extract molecular information, and a multi-layer neural network and a convolutional neural network are input for deep learning training to obtain an organic fluorescent small molecule optical property prediction model; the characteristic information of the to-be-predicted organic fluorescent micromolecules and the experimental solvent thereof are input into the trained organic fluorescent micromolecule optical property prediction model so as to predict the optical property of the to-be-predicted organic fluorescent micromolecule. The method can accurately predict the optical properties (the average relative error is less than 5%) of the organic fluorescent small molecules, so that the development efficiency of the organic fluorescent small molecules is improved.

Description

technical field [0001] The invention relates to the cross field of computer science and chemical fluorescent probes, in particular to a method for predicting the optical properties of small organic fluorescent molecules based on deep neural networks. Background technique [0002] With the development of life sciences and research needs, fluorescence imaging technology has gradually become one of the important means of qualitative and quantitative detection. Among them, organic fluorescent small molecules have the characteristics of small structure, easy chemical transformation, and excellent optical properties. Their design and synthesis have always been one of the research hotspots in the field of fluorescence imaging. At present, we have a certain understanding of the relationship between the structure and optical properties of organic fluorescent small molecules, but there are still limited reports that can accurately predict their optical properties based on molecular st...

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Application Information

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IPC IPC(8): G16C20/30G06N3/04G06N3/08
CPCG16C20/30G06N3/08G06N3/045
Inventor 钱玲慧严佳奇苗晓晔刘悦邵瑾宁吴洋洋廖佳宇严泽伊
Owner ZHEJIANG UNIV
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