A generative neural network based on a hetero-encoder model for de novo design of potential anticancer drugs: application to Bcr-Abl tyrosine kinase
Objectives. The problem of developing a generative hetero-encoder model for computer-aided design of potential inhibitors of Bcr-Abl tyrosine kinase, an enzyme whose activity is the pathophysiological cause of chronic myeloid leukemia, is being solved.Methods. A generative hetero-encoder model was d...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Russian |
| Published: |
National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2023-09-01
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| Series: | Informatika |
| Subjects: | |
| Online Access: | https://inf.grid.by/jour/article/view/1259 |
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| Summary: | Objectives. The problem of developing a generative hetero-encoder model for computer-aided design of potential inhibitors of Bcr-Abl tyrosine kinase, an enzyme whose activity is the pathophysiological cause of chronic myeloid leukemia, is being solved.Methods. A generative hetero-encoder model was designed based on the recurrent and fully connected neural networks of direct propagation. Training and testing of this model were carried out on a set of chemical compounds containing 2-arylaminopyrimidine, which is present as the main pharmacophore in the structures of many small-molecule inhibitors of protein kinases.Results. The developed neural network was tested in the process of generating a wide range of new molecules and subsequent analysis of their chemical affinity for Bcr-Abl tyrosine kinase using molecular docking methods.Conclusion. It is shown that the developed neural network is a promising mathematical model for de novo design of small molecules which are potentially active against Bcr-Abl tyrosine kinase and can be used to develop effective broad-spectrum anticancer drugs. |
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| ISSN: | 1816-0301 |