Intelligent drug molecule generation method based on reinforcement learning and docking

A technology of reinforcement learning and drug molecules, applied in the field of intelligent generation of drug molecules based on reinforcement learning and docking, can solve the problems of the generation speed, effectiveness and molecular activity of candidate compounds, and achieve the effect of optimizing the activity and reducing the chemical space.

Active Publication Date: 2021-10-08
OCEAN UNIV OF CHINA
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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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  • Intelligent drug molecule generation method based on reinforcement learning and docking
  • Intelligent drug molecule generation method based on reinforcement learning and docking
  • Intelligent drug molecule generation method based on reinforcement learning and docking

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Embodiment 1

[0040] The main goal of this example is: generation of active compounds against the Mpro target of the new crown, based on an initial set of lead compounds, and then improving and optimizing these molecules by replacing some fragments of them, resulting in an Mpro target with the desired properties new active compounds. This embodiment is based on an Actor-critic reinforcement learning model and a docking simulation method for generating new drug molecules with optimal properties. The technical solution of this embodiment is described in detail below.

[0041] A method for generating drug molecule intelligence based on Actor-critic reinforcement learning model and docking, which specifically includes the following steps:

[0042] Step 1. Construct a virtual fragment combination library for drug design.

[0043] The virtual fragment combinatorial library of drug molecules is constructed by fragmenting a set of molecules. The virtual fragment library of this example is jointl...

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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...

Claims

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Application Information

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IPC IPC(8): G16C20/50G16C20/70G16C20/90
CPCG16C20/50G16C20/70G16C20/90Y02A90/10
Inventor 魏志强王茜刘昊李阳阳王卓亚
Owner OCEAN UNIV OF CHINA
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