Drug molecule property prediction method, device and equipment based on comparative learning

Abstract

The invention discloses a drug molecule property prediction method, device and equipment based on comparative learning, relates to the technical field of artificial intelligence, and can solve the technical problems of low efficiency and poor prediction performance of drug molecule property prediction at present. Comprising the following steps: generating a target molecular map structure of a target drug molecule according to a chemical molecular structure, and generating a target three-dimensional conformation of the target drug molecule; using the trained graph neural network model to determine a first feature vector corresponding to the target molecular graph structure; a second feature vector corresponding to the target three-dimensional conformation is determined through a trained convolutional neural network model, and the graph neural network model and the convolutional neural network model are obtained through comparative learning and joint training of a positive sample pair and a negative sample pair; and constructing a third feature vector according to the first feature vector and the second feature vector, and inputting the third feature vector into the trained property prediction model to obtain a property prediction result of the target drug molecule.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a method, device and equipment for predicting drug molecular properties based on comparative learning. Background technique [0002] The drug development cycle is long, the investment is large and it is extremely risky. In order to fully explore the laws behind drug molecules and speed up the discovery of drug development, researchers in the field of drug development have tried to introduce machine learning methods into medicinal chemistry research since this century. Accurate and efficient molecular property prediction models can greatly Minimize dependence on experiments, reduce costs, and speed up progress. [0003] At present, the properties of drug molecules can be predicted based on molecular fingerprints and molecular descriptors. However, this method requires a lot of professional knowledge for optimal design, and lacks versatility and scalabili...

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Problems solved by technology

[0004] In view of this, this application provides a method, device and equipment for predicting drug molecular properties based on comparative learni

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