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A Molecular Property Prediction Method Based on Artificial Neural Network

A technology of artificial neural network and prediction method, applied in the direction of biological neural network model, chemical property prediction, molecular design, etc., can solve problems such as unusable data, achieve high precision, good speed and precision, and high efficiency

Active Publication Date: 2022-05-13
UNIV OF SCI & TECH OF CHINA
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

At the same time, this research field has accumulated a lot of relevant data, but most of the methods cannot use these existing data

Method used

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  • A Molecular Property Prediction Method Based on Artificial Neural Network
  • A Molecular Property Prediction Method Based on Artificial Neural Network
  • A Molecular Property Prediction Method Based on Artificial Neural Network

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Embodiment 1

[0080] Take the U of the following three molecules 0 Forecasting as an example, they all come from the commonly used QM9 data set in the world, and the unit is eV. We use the QM9 data set as the training set, the training method is as above, and then use the model obtained after training to predict the following molecules. The standard to measure the error is the absolute error, which is the absolute value of the difference between the predicted value and the actual value.

[0081] (1)CH 4 The true value of is -17.1717476eV, the predicted value is -17.1681695eV, and the error is 0.0035781eV. (2) NH 3 The true value of is -12.0055513eV, the predicted value is -12.0187658eV, and the error is 0.0132145eV.

[0082] (3) The actual value of HOH is -9.2401279eV, the predicted value is -9.2371538eV, and the error is 0.0029741eV.

[0083] And the average error of each attribute prediction of this method on the entire QM9 data set is given in the table below.

[0084]

[0085] ...

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Abstract

The present invention provides a method for predicting molecular properties based on artificial neural network, comprising: S1) preprocessing molecular data: obtaining atomic space representation and atomic composition representation through the method of graph data structure representation; S2) establishing a model: The atomic space representation and the atomic composition representation are passed through a multi-layer convolutional neural network to obtain representations of molecular levels, and the representations of molecular levels are combined to obtain a model; S3) Predict molecular properties according to the model. Compared with the prior art, the present invention utilizes the multi-level convolutional neural network, which can use the information of the existing data and the multi-level structure of molecules to learn the relationship between molecular properties and spatial composition, and to predict the related properties of unknown molecules , so it has better speed and accuracy.

Description

technical field [0001] The invention belongs to the technical field of materials science, and in particular relates to a method for predicting molecular properties based on an artificial neural network. Background technique [0002] From drug development to material development, molecular discovery is inseparable. In order to find molecules with specific properties to meet the needs of applications, the general method is to traverse an unknown set of possible molecules (called chemical space), during the traversal process, researchers use various methods to predict molecular properties properties, and if a molecule is found to meet the requirements, it is recorded for further study. For example, predictions of the energy properties of molecules can help researchers find stable molecules. [0003] However, the chemical space is often very large, and a widely used chemical space has more than 160 billion molecules. Therefore, a rapid method for determining molecular propert...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16C20/30G16C20/40G16C20/50G06N3/04
CPCG06N3/045
Inventor 刘淇陈恩红陆承镪王超黄振亚
Owner UNIV OF SCI & TECH OF CHINA
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