Generative Autoencoders for Designing Novel Small-Molecule Compounds as Potential SARS-CoV-2 Main Protease Inhibitors
15th International Conference on Pattern Recognition and Information Processing, PRIP 2021
; 1562 CCIS:120-136, 2022.
Article
in English
| Scopus | ID: covidwho-1777668
ABSTRACT
Two generative autoencoder models for designing novel drug-like compounds able to block the catalytic site of the SARS-CoV-2 main protease (MPro) critical for mediating viral replication and transcription were developed using deep learning methods. To do this, the following steps were performed (i) architectures of two neural networks were constructed;(ii) a virtual compound library of potential anti-SARS-CoV-2 MPro agents for training two neural networks was formed;(iii) molecular docking of all compounds from this library with MPro was made and calculations of the values of binding free energy were carried out;(iv) two neural networks were trained followed by estimation of the learning outcomes and work of two autoencoders involving several generation modes. Validation of autoencoders and their comparison revealed the best combination of the neural network architecture with the generation mode, which allows one to generate good chemical scaffold for the design of novel antiviral drugs with suitable pharmaceutical properties. © 2022, Springer Nature Switzerland AG.
Anti-SARS-CoV-2 drugs; Binding free energy calculations; Deep learning; Generative autoencoder; Main protease; Molecular docking; SARS-CoV-2; Semi-supervised learning; Virtual screening; Binding energy; Digital libraries; Diseases; E-learning; Molecular modeling; Network architecture; Scaffolds; Anti-SARS-CoV-2 drug; Auto encoders; Binding free energy; Binding free energy calculation; Free-energy calculations; SARS
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
15th International Conference on Pattern Recognition and Information Processing, PRIP 2021
Year:
2022
Document Type:
Article
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