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High-throughput drug virtual screening system based on molecular fingerprints and deep learning

A deep learning and virtual screening technology, applied in the analysis of two-dimensional or three-dimensional molecular structure, neural learning methods, informatics, etc., can solve the problems of long cycle, high risk, large investment, etc., to achieve smooth system and easy use. , to accelerate the effect of drug discovery

Inactive Publication Date: 2019-11-12
GUANGDONG INST OF MICROBIOLOGY GUANGDONG DETECTION CENT OF MICROBIOLOGY
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Drug research and development has the characteristics of large investment, high risk, and long cycle. Generally speaking, a drug research and development cycle is more than 10 years, and the research and development investment is hundreds of millions of dollars, and it shows an increasing trend year by year.

Method used

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  • High-throughput drug virtual screening system based on molecular fingerprints and deep learning
  • High-throughput drug virtual screening system based on molecular fingerprints and deep learning
  • High-throughput drug virtual screening system based on molecular fingerprints and deep learning

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Embodiment Construction

[0017] The present invention proposes a virtual screening system based on molecular fingerprints and deep learning, including an online modeling subsystem of a deep learning model and an online virtual screening subsystem, realizing the automation of the entire process from the construction and training of a deep learning model to model-based virtual screening .

[0018] The deep learning online modeling subsystem mainly includes an online modeling module and a model result module.

[0019] Online Modeling Modules:

[0020] 1) Choose the type of modeling, qualitative classification model or quantitative regression model.

[0021] 2) In the data preparation module, select the target point to be modeled according to the above model type. Currently, the classification model supports 1251 targets, and the regression model supports 1814 targets. At the same time, users can also upload their own data, and support sdf file format upload.

[0022] 3) The selection of molecular fin...

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Abstract

The invention discloses a high-throughput drug virtual screening system based on molecular fingerprints and deep learning. The system comprises a deep learning model online modeling subsystem and an online virtual screening subsystem. The molecular fingerprints are combined with a deep neural network method to establish the high-throughput drug virtual screening system, wherein the system is internally provided with a screening library with the structural diversity, online automatic establishment of a deep learning model and virtual screening are achieved, the system is smooth, the use is convenient, and medicinal chemistry workers and people working on drug discovery are helped to perform rapid screening on their own targets to obtain potential active compounds and speed up the drug discovery.

Description

technical field [0001] The invention relates to the technical field of drug research and development, in particular to a high-throughput drug virtual screening system based on molecular fingerprints and deep learning. Background technique [0002] Drug research and development has the characteristics of large investment, high risk and long cycle. Generally speaking, a drug research and development cycle is more than 10 years, and the research and development investment is hundreds of millions of dollars, and it shows an increasing trend year by year. In recent years, deep learning technology has made major breakthroughs in areas such as unmanned driving, speech recognition, and image recognition. At the same time, deep learning technology has also made important progress in the field of biomedicine. Using the advantages of deep learning in image recognition, a series of diagnostic applications based on deep learning in skin cancer, congenital cataracts, and children's autism...

Claims

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Application Information

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IPC IPC(8): G16B15/30
CPCG16B15/30G16B35/20G06N3/08G06N3/04
Inventor 谢黎炜刘志红刘秉东潘潇寒韩木兰许国焕
Owner GUANGDONG INST OF MICROBIOLOGY GUANGDONG DETECTION CENT OF MICROBIOLOGY
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