Intelligent drug molecule generation method based on reinforcement learning and docking

Abstract

The invention relates to an intelligent drug molecule generation method based on reinforcement learning and docking, and belongs to the technical field of medicinal chemistry and computers. The method comprises the following steps: 1) constructing a virtual fragment combinatorial library for a drug design; 2) calculating fragment similarity and carrying out molecular fragment coding; and 3) generating and optimizing molecules based on an actor-critic model of reinforcement learning. According to the method, on the basis of a lead compound, the search chemical space is reduced. Transformer modeling is adopted through the actor-critic model of reinforcement learning, position information of molecular fragments is introduced, relative or absolute position information of the fragments in molecules is stored, and parallelization training is achieved. In addition, an award mechanism further optimizes the activity of generated molecules by establishing a single-layer perceptron model.

Description

technical field [0001] The invention relates to the fields of medicinal chemistry and computer technology, in particular to an intelligent generation method of drug molecules based on reinforcement learning and docking. Background technique [0002] Designing and manufacturing safe and effective compounds is key in the medicinal chemistry profession. This is a long, complex and difficult multi-parameter optimization process in terms of money and time. Promising compounds have a high risk (>90%) of failing in clinical trials, resulting in unnecessary waste of resources. The average cost of bringing a new drug to market is now well over $1 billion, and the average time from discovery to market is 13 years. In pharmaceuticals, the average time from discovery to commercial production can be longer, such as 25 years for high-energy molecules. A critical first step in molecular discovery is generating a pool of candidates for computational study or synthesis and characteriza...

AI Technical Summary

Problems solved by technology

However, current methods still need to be improved in terms of the generation speed, effectiveness and molecular activity of candidate compounds

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