Small molecule drug virtual screening method based on deep migration learning and application thereof

A technology of virtual screening and transfer learning, which is applied in the field of virtual screening of small molecule drugs based on deep transfer learning, which can solve problems such as difficulty in obtaining useful ligand samples and insufficient information on active ligand samples.

Active Publication Date: 2019-11-15
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] Purpose of the invention: The present invention provides a method for virtual screening of small molecule drugs based on deep transfer learning, which is used to solve the problem of insufficient sample information of known active ligands in the virtual screening of small molecule drugs and the difficulty of obtaining a useful virtual screening model

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  • Small molecule drug virtual screening method based on deep migration learning and application thereof
  • Small molecule drug virtual screening method based on deep migration learning and application thereof
  • Small molecule drug virtual screening method based on deep migration learning and application thereof

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[0050] Table 1

[0051]

[0052] As shown in Table 1 above, this is the data set we obtained after biotechnology screening. They are grouped into one group because they all belong to homologous proteins and have a common family. Here we call it Group A, where A1-A6 It is our target domain, that is, the small sample data set we are targeting. The number of them ranges from more than one hundred to more than one thousand, which is very unfavorable for us to do deep learning, so we found our source domain again, namely AS1 , AS2, they have sample sizes in the thousands. What we have to do is to use the source domain to improve the training effect of the target domain. The specific implementation steps are as follows:

[0053] 1. Take the source domain as input and input it into our general tool demo_new1 for ligand-based virtual screening for training:

[0054] (1) Initialize network parameters, including weight matrix W, molecular fingerprint f 0 ;

[0055] (2) Randomly s...

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Abstract

The invention discloses a small molecule drug virtual screening method based on deep migration learning and application thereof. A source domain is used as an input to be trained, converged and derived to obtain a weight matrix; a target domain is input into an improvement tool to serve as the initialization weight of the target domain; fine adjustment, training and convergence are conducted on the initialization weight and data in the target domain sequentially; a biological activity value of interaction of a lead compound and a drug target in the target domain is predicted, a target domain molecular fingerprint and a predicted value are obtained, and an evaluation index root mean square error and a correlation coefficient of a predicted result are output; the target domain is subjected to fine adjustment by repeating above steps, and the weight matrix of the source domain helps the target domain build a model. According to the small molecule drug virtual screening method and the application thereof, the effective virtual screening model can still be obtained under the condition that the information of a known active ligand sample is insufficient, and does not need to rely on a large number of data samples.

Description

technical field [0001] The present invention relates to a small molecule drug screening method and its application, in particular to a small molecule drug virtual screening method based on deep transfer learning and its application. Background technique [0002] Small molecule drug screening often requires high-throughput experimental techniques to measure the biological activity value of the interaction between the target and the compound in a huge number of compound databases to screen lead compounds. However, the experimental method is time-consuming and labor-intensive. What's worse, the number of compounds that are often available is very limited, and not all drug targets are suitable for high-throughput screening experiments. Therefore, computational-based virtual screening, which simulates the interaction between the target of interest and drug candidates, has been widely used in small molecule drug design. [0003] Computational-based virtual screening can be divide...

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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 吴建盛陈严小虎胡海峰
Owner NANJING UNIV OF POSTS & TELECOMM
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