Drug molecule generation method based on adversarial imitation learning

Abstract

The invention discloses a drug molecule generation method based on adversarial imitation learning, drug molecules are generated based on adversarial imitation learning and multi-task reinforcement learning, and the method comprises the following steps: constructing an effective drug molecule library; establishing an improved drug molecule generation model: designing and realizing a multi-task reinforcement learning module, and designing and realizing an adversarial imitation learning module; pre-training the model; executing a drug molecule generation process; and generating a candidate drug molecule result. By adopting the technical scheme provided by the invention, optimization of biochemical properties of drug molecules can be effectively promoted, the stability of model training is improved, and better drug molecules are obtained.

Description

technical field [0001] The present invention relates to the cross technical field of computer artificial intelligence and new drug molecule design, in particular to a drug molecule generation method based on confrontational imitation learning, which is a method for designing new drug molecules based on confrontational imitation learning, deep reinforcement learning and multi-task optimization. The method is suitable for the design and generation of candidate drug molecules in the process of new drug discovery. Background technique [0002] The development of new drugs is costly, takes a long time and has a low success rate. The screening of candidate drug molecules is the key link in the early stage. The introduction of computer-aided design and the latest artificial intelligence technology has greatly improved the efficiency of molecular screening. However, traditional computer screening methods mostly target existing compounds, or screen them based on structure or propert...

AI Technical Summary

Problems solved by technology

First of all, the training process of the deep generative model is extremely unstable, and the problem of mode collapse is common, resulting in the generation of a large number of identical molecules, which limits the novelty and specificity of the generated molecules, and thus cannot meet the actual needs of the medical field.

Secondly, since the rewards in reinforcement learning are often obtained after the final molecule is generated, there are problems of reward delay and sparseness, which seriously affect the adjustment of rei

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