Intelligent screening system of mitochondrial effector molecules and construction method and application thereof

By constructing an intelligent screening system for mitochondrial effector molecules based on support vector machines, the problem of time-consuming and labor-intensive traditional methods has been solved, achieving efficient screening of mitochondrial effector molecules and improving screening efficiency and accuracy.

CN115206437BActive Publication Date: 2026-03-31XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202210736387.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-27
Publication Date
2026-03-31
Estimated Expiration
2042-06-27

AI Technical Summary

Technical Problem

Traditional biological screening methods are time-consuming and resource-intensive in screening mitochondrial effector molecules, which is difficult to meet the needs of modern pharmaceutical research and development.

Method used

We constructed an intelligent screening system for mitochondrial effect molecules based on machine learning. We used a support vector machine model to predict large molecular datasets and screen out molecules with potential mitochondrial effects. By collecting target protein information, processing data, and training the model, we established an intelligent screening model for mitochondrial effect molecules.

Benefits of technology

It reduces parameter tuning and data processing time, improves screening efficiency, and can efficiently screen mitochondrial-targeting effector molecules from large datasets, making it suitable for research in the field of mitochondria.

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Abstract

The application relates to an intelligent screening system of a mitochondrion effect molecule, a construction method and application thereof, and belongs to the technical field of molecular biology. The construction method of the intelligent screening system of the mitochondrion effect molecule comprises the following steps: 1, establishing a target protein library; 2, obtaining a data set of the mitochondrion effect molecule; 3, adopting Morgan molecular fingerprint to characterize the mitochondrion effect molecule in the data set, carrying out deduplication and decontamination processing, and then carrying out molecular similarity processing to obtain an input set of a model; and 4, taking accuracy and AUC value as evaluation indexes to construct a support vector machine model. The application utilizes the support vector machine model to carry out prediction in a large amount of molecular data set, and gives a molecule with a high probability score which may have an effective effect on mitochondria, the model is helpful for researchers in the field of mitochondria to reduce parameter adjustment time and improve work efficiency.
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