Drug molecule generation method based on regularization variation automatic encoder

An autoencoder, drug molecule technology, applied in the intersection of computer artificial intelligence and new drug molecule design, can solve the problems of difficulty in designing immediate rewards, relying on the smoothness of the hidden vector space, and increasing difficulty in calculation and convergence. The effect of generating excellent results, reducing network complexity, and reducing dependencies

Active Publication Date: 2020-04-07
PEKING UNIV
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Problems solved by technology

However, the method based on reinforcement learning needs to introduce an additional network, which increases the difficulty of calculation and convergence, and it is difficult to design a reasonable immediate reward; the method

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  • Drug molecule generation method based on regularization variation automatic encoder
  • Drug molecule generation method based on regularization variation automatic encoder
  • Drug molecule generation method based on regularization variation automatic encoder

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Embodiment Construction

[0046] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0047] The research and development of new drugs is costly and takes a long time. One of the key links is the screening of candidate drug molecules. The introduction of artificial intelligence technology can effectively improve the screening efficiency, but screening-based methods are limited to existing compounds, and the scope is limited. Emphasis is placed on novel molecular generation methods.

[0048] The present invention proposes a molecular generation model based on a deep generative model, which involves the intersection of computer artificial intelligence and medical molecular design. Its core idea is to introduce the graph neural network and property regularization into the deep generative model at the same time, which can effectively use the intuition of graph representation. At the same ...

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Abstract

The invention discloses a drug molecule generation method based on a regularization variation automatic encoder, and the method comprises the following steps: expressing drug molecules as graph data,and establishing a drug molecule generation model comprising an encoder and a decoder by using a variation automatic encoder framework; enabling the encoder to directly encode an input drug moleculardiagram by using a diagram neural network; wherein the decoder adopts a multi-layer perceptron, the optimization target comprises reconstruction loss, KL loss and property regularization loss, and theproperty regularization loss is estimated by utilizing Monte Carlo sampling. Candidate drug molecules generated by adopting the technology disclosed by the invention are higher in effectiveness and more excellent in molecule property.

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 regularized variational autoencoder, which is a method based on graph neural network, deep generation model and property target regularization The method for designing new drug molecules is applicable to 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 properties. The new...

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Application Information

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IPC IPC(8): G16C20/50G16C20/70
CPCG16C20/50G16C20/70
Inventor 吕肖庆李昕张昊汤帜
Owner PEKING UNIV
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