Molecular structure prediction method based on graph convolutional network

Abstract

The invention belongs to the technical field of machine learning, and particularly relates to a molecular structure prediction method based on a graph convolutional network. According to the method, a molecular graph and a molecular complete graph are constructed according to SMILES of an input molecule, a network model with two branches is correspondingly constructed, wherein one branch adopts an MLP for edge prediction, and the other branch comprises a graph convolutional network and an MLP and is used for extracting overall structure features of the branches. The molecular structure features are extracted by using graph convolution, and the overall structure features of molecules can be well extracted, so that the structure is better predicted; besides, a double-branch model design is used, and the problem that a complete graph destroys molecular structure information is solved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a molecular structure prediction method based on a graph convolutional network. Background technique [0002] Molecular structure is the basis of molecular research, because the relationship between molecular microstructure and various macroscopic chemical properties including chemical reactions and various physical properties is inseparable. Therefore, people began to study The study of molecular structure summarizes the regularity of material structure from the molecular configuration and motion characteristics of various known chemical substances to explain the formation and properties of substances. [0003] The earliest determination of molecular structure was mainly through experimental observation methods, such as microwave spectroscopy, X-ray diffraction, electron diffraction and neutron diffraction, among which microwave spectroscopy is often used to...

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Problems solved by technology

Among them, the molecular structure in the QM9 data set has been optimized by multiple methods and has high reliability, but it needs to switch between several methods, which is cumbersome and time-consuming. Although the latter methods are faster in calculation, they are often compared. rough or requires prior knowledge

[0005] In recent years, with the development of machine learning, more and more machine learning methods have been applied to the fields of materials and chemistry and have achieved success, which greatly reflects the advantages of machine learning methods in quantum chemical calculations. The emerging graph neural network is very suitable for feature extraction of molecular graph representations, but existing methods usually use it to predict the properties of molecules rather than structures. For example, Chen et al. developed a graph called MEGNet The network model accurately predicts the properties of molecules and crystals. Louis et al. proposed a new GATGNN model to solve the problem that many GNN models cannot effectively distinguish the contributions of different atoms when predicting properties, and achieved quite good results. predictive performance

However, the use of graph neural networks to predict molecular structures has not yet seen relevant public reports.

[0006] Because the molecular structure plays a very important role in the research, understanding and synthesis of molecules, especially in the field of new drug development, knowing the molecular structure of drugs is very helpful for the prediction of drug molecular properties, target prediction and chemical reaction prediction, so as to guide the synthesis of new molecules , and the determination of molecular structure by the experimental method is complicated, costly, and requires manpower and material resources, and it is impossible to determine the newly designed molecules that have not yet existed. In the traditional calculation method, there is a problem that the calculation accuracy and time cost cannot be well balanced. problem of relationship

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