Training method and device of molecular graph reconstruction model and electronic equipment

Abstract

The invention provides a molecular graph reconstruction model training method and device and electronic equipment, the method is suitable for the artificial intelligence medicine field, and the molecular graph reconstruction model comprises an encoder, a decoder and a graph matching module; in the training method provided by the invention, on one hand, a graph matching module is designed as a pre-training module, and a relation matrix output by the graph matching module is designed to be used for calculating the reconstruction loss between a sample molecular graph and a reconstruction molecular graph, on the other hand, a representation vector of the sample molecular graph output by an encoder is designed as an input vector of a decoder, and the reconstruction loss between the sample molecular graph and the reconstruction molecular graph is calculated. According to the molecular graph reconstruction model training method provided by the invention, the molecular graph reconstruction model can be suitable for a molecular discovery process, the matching complexity and resource consumption can be reduced, the matching accuracy is improved, and the guiding effect of a graph matching module on the molecular graph reconstruction model is further improved. In addition, the method provided by the invention can also improve the practicability and reconstruction effect of the molecular graph reconstruction model.

Description

technical field [0001] The embodiments of the present application relate to the field of artificial intelligence in medicine, and more specifically, relate to a training method, device and electronic equipment of a molecular graph reconstruction model. Background technique [0002] The goal of molecular discovery is to find candidate molecules with desired chemical properties, which is a lengthy, costly, and high failure rate process. [0003] Since graph generative models can explore large latent spaces in a data-driven manner, they have shown great potential in accelerating the molecular discovery process. [0004] As far as those skilled in the art know, the graph generation model based on an autoencoder is a model that can be used to generate a reconstructed graph. Specifically, the sample graph can be encoded into the hidden space first, and then the decoder can be used to decode Predict node features and graph topology to reconstruct sample graphs. However, due to th...

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Problems solved by technology

However, due to the invariance of the graph permutation, the reconstruction loss adopted by the graph generation model cannot supervise whether the output reconstructed sample is a candidate molecule with the desired chemical properties, which makes the graph generation model not suitable for the molecular discovery process.

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