A method, apparatus, and equipment for predicting oral absorption rate and optimizing compound structure.

By introducing molecular structural parameters and a multiple linear regression model, the problem of inaccurate oral absorption rate prediction caused by the failure to consider molecular flexibility in existing technologies is solved, and accurate prediction and structural optimization of highly flexible compounds are achieved.

CN121938498BActive Publication Date: 2026-07-03PEKING UNIV INST OF ADVANCED AGRI SCI +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
PEKING UNIV INST OF ADVANCED AGRI SCI
Filing Date
2026-03-30
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing methods for predicting oral absorption rates do not adequately consider structural factors related to molecular flexibility, resulting in insufficient accuracy in predicting highly flexible compounds.

Method used

By introducing molecular structural parameters, a multiple linear regression model is constructed. Combining lipid solubility, polarity, electronic structure, and in vitro permeability parameters, an oral absorption rate prediction model is established. A progressive modeling approach is used to analyze the influence of molecular flexibility and electronic properties layer by layer.

Benefits of technology

It significantly improves the accuracy and reliability of predicting the oral absorption rate of highly flexible compounds, providing more comprehensive and accurate data support for compound structure optimization, and making the direction of compound structure optimization more precise and convenient.

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Abstract

This invention relates to the field of cheminformatics technology, and discloses a method, apparatus, and device for predicting oral absorption rate and optimizing compound structures. The method includes: acquiring a compound sample dataset, which includes: multi-dimensional feature sample parameters corresponding to each compound sample and a reference oral absorption rate; the multi-dimensional feature sample parameters include at least one of the following: lipophilicity parameters, polarity parameters, electronic structure parameters, and in vitro permeability parameters, and also include molecular structure parameters; using the multi-dimensional feature sample parameters as input and the reference oral absorption rate as the output label, training a pre-constructed initial model to obtain an oral absorption rate prediction model. This invention incorporates molecular structure parameters into the construction of the oral absorption rate prediction model, which can significantly improve the model's accuracy in predicting the oral absorption rate of compounds with complex structures and high flexibility, providing more accurate data support for subsequent compound structure optimization.
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