Virtual drug screening method and device based on molecular docking

A technology of molecular docking and screening methods, applied in molecular design, biological neural network models, neural architectures, etc., can solve the problems of limiting wide application and unstable output simulation effects, and achieve strong robustness
CN111199779APending Publication Date: 2020-05-26中科曙光国际信息产业有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
中科曙光国际信息产业有限公司
Publication Date
2020-05-26

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Abstract

The invention provides a virtual drug screening method and device based on molecular docking. The method comprises the following steps: inputting drug molecular information; converting the drug molecule information into n-dimensional floating point type data information; outputting the information of each atom of the drug molecule according to the n-dimensional floating point type data informationby adopting an LSTM network, and summarizing the information of each atom to obtain a vector representing the characteristics of the drug molecule; assigning a weight to the vector representing the characteristics of the drug molecule through an attention network to obtain a vector model of the drug molecule; and screening the activity of the drug molecules according to the vector model. According to the scheme, the AUC value can be increased by 2%, and higher robustness is achieved.
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Description

technical field

[0001] The invention relates to a molecular docking-based virtual drug screening method and device. Background technique

[0002] New drug research and development is a very time-consuming, laborious and costly project. In recent years, computer-aided virtual drug screening technology has been widely developed, and one of the widely used technologies is virtual drug screening based on the principle of molecular docking. The basic principle of molecular docking technology is to select potential drug molecules to simulate the binding of pathogenic target proteins, but each drug molecule may have thousands of binding methods, and it is even more difficult to obtain mathematical functions that can accurately evaluate different binding methods. This is the core problem of current molecular docking technology.

[0003] With the success of machine learning-based algorithms in solving this problem in recent years, such as support vector machines, random forests, et...

Claims

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