Massive Feature Screening Method for Ligand Molecules in Drug Design

A feature screening and ligand molecule technology, applied in the field of computer-aided drug design, can solve the problems of high time consumption and achieve the effect of increasing comprehensibility and improving learning efficiency
CN106778032BActive Publication Date: 2019-06-04NANJING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIV OF POSTS & TELECOMM
Publication Date
2019-06-04

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Abstract

The invention discloses a ligand molecule massive characteristic screening method in drug design. In the drug molecule virtual screening based on the ligand, dimensionality (every dimensional characteristic represents a substructure) of the ligand molecule fingerprint feature generated by using the most popular ECFP method at present is massive and even ten thousands of dimensions due to the large number of ligand molecule, so the method can meet the problem of 'dimensionality disaster' in actual task. The method screens the massive ECFP molecule fingerprint features by using the LASSO method based on EDPP rule, and acquires related characteristics of the ligand molecule by using a robustness selecting method. The activity of the ligand molecule is often related to a few number of substructure; the method can rapidly and largely remove uncorrelated characteristics, select related characteristics of robustness, solve the problem of the 'dimensionality disaster', acquire the substructure related to the ligand activity and push the wider application of the ECFP method in the drug design.
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Description

Technical field

[0001] The invention relates to a method for screening ligand molecular characteristics based on machine learning, and belongs to the technical field of computer-aided drug design. Background technique

[0002] In recent years, how to improve the effectiveness of virtual drug screening has become an urgent problem for pharmaceutical companies to solve. Since a large number of biochemical experiments provide sufficient data, machine learning methods can use these data to help solve problems.

[0003] Virtual drug screening is divided into two types: target structure and ligand-based methods. Drug virtual screening based on target structure simulates the physical interaction between the compound and the target to determine whether there may be drug effects, such as molecular docking methods. The ligand-based method is mainly to use existing data to predict the activity of the compound when the target structure is unknown. The most important thing for this kind of m...

Claims

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