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Method for predicting ASK1 inhibitory activity based on quantitative structure-activity relationship model

A quantitative structure-activity relationship and inhibitory activity technology, applied in chemical machine learning, molecular design, chemical data mining, etc., can solve problems that cannot be displayed intuitively, and achieve experimental cost saving, high model accuracy and high model accuracy Effect

Pending Publication Date: 2022-06-07
DALIAN UNIV OF TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

The vast majority of machine learning methods are black-box models that cannot be displayed intuitively

Method used

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  • Method for predicting ASK1 inhibitory activity based on quantitative structure-activity relationship model
  • Method for predicting ASK1 inhibitory activity based on quantitative structure-activity relationship model
  • Method for predicting ASK1 inhibitory activity based on quantitative structure-activity relationship model

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Embodiment Construction

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.

[0049] The method will be further described below through specific examples and in conjunction with tables.

[0050] A method for predicting ASK1 inhibitory activity based on a quantitative structure-activity relationship model, comprising the following steps:

[0051] S1. Collection of data

[0052] Collect experimental data by reviewing the literature. A dataset of 232 ASK1 small molecule inhibitors was established. 232 small molecule inhibitors from 16 papers and patents were selected. The data collected include the structure of the small molecule and the value IC50 for its ability to inhibit activity.

[0053] S2. Dataset processing...

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Abstract

The invention relates to the field of drug activity prediction methods, in particular to a method for predicting ASK1 inhibitory activity based on a quantitative structure-activity relationship model. The method comprises the specific steps of collection of the ASK1 small-molecule inhibitor, processing and optimization of a data set, division of the data set, screening of descriptors, establishment of a multiple linear regression model and verification of the model. Through the method, a prediction result can be quickly obtained, the method can be applied to an alternative library with a large number of inhibitors for screening, the model is high in accuracy and stable, has generality and wide applicability, and can quickly find a proper small-molecule inhibitor. The experiment cost is saved, and unnecessary resource waste is avoided. And a powerful basis is provided for the development of new drugs.

Description

technical field [0001] The invention relates to the technical field of drug activity prediction methods, in particular to a method for predicting ASK1 inhibitory activity based on a quantitative structure-activity relationship model. Background technique [0002] The mitogen-activated protein kinase pathway (MAPK pathway) is the most widely used and critical stress response signaling pathway in eukaryotes and plays a key role in the regulation of various cellular processes. Apoptosis signal-regulated kinase 1 (ASK1) is a member of the mitogen-activated protein kinase kinase (MAP3K) family, which is responsible for the activation of the p38 MAP kinase and c-Jun N-terminal kinase (JNK) pathways. Activation of the kinase ASK1 is stimulated by various cellular stressors, including cytotoxic cytokines, reactive oxygen species (ROS), and endoplasmic reticulum stress. Studies have shown that ASK1 is not only involved in the regulation of cell death, but also in cytokine responses,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/50G16C20/70
CPCG16C20/50G16C20/70
Inventor 姜珊
Owner DALIAN UNIV OF TECH
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