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An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer

In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule anti-tumor agents. Our approach was exemplified in the context of combatting lung cancer. In the initial phase, target identification for therapeutic...

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Autores principales: Chebanov, Dmitrii K., Misyurin, Vsevolod A., Shubina, Irina Zh.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666046/
https://www.ncbi.nlm.nih.gov/pubmed/38025397
http://dx.doi.org/10.3389/fbinf.2023.1225149
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author Chebanov, Dmitrii K.
Misyurin, Vsevolod A.
Shubina, Irina Zh.
author_facet Chebanov, Dmitrii K.
Misyurin, Vsevolod A.
Shubina, Irina Zh.
author_sort Chebanov, Dmitrii K.
collection PubMed
description In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule anti-tumor agents. Our approach was exemplified in the context of combatting lung cancer. In the initial phase, target identification for therapeutic intervention was accomplished. Leveraging deep learning, we scrutinized gene expression profiles, focusing on those associated with adverse clinical outcomes in lung cancer patients. Augmenting this, generative adversarial neural (GAN) networks were employed to amass additional patient data. This effort yielded a subset of genes definitively linked to unfavorable prognoses. We further employed deep learning to delineate genes capable of discriminating between normal and tumor tissues based on expression patterns. The remaining genes were earmarked as potential targets for precision lung cancer therapy. Subsequently, a dedicated module was formulated to predict the interactions between inhibitors and proteins. To achieve this, protein amino acid sequences and chemical compound formulations engaged in protein interactions were encoded into vectorized representations. Additionally, a deep learning-based component was developed to forecast IC(50) values through experimentation on cell lines. Virtual pre-clinical trials employing these inhibitors facilitated the selection of pertinent cell lines for subsequent laboratory assays. In summary, our study culminated in the derivation of several small-molecule formulas projected to bind selectively to specific proteins. This algorithmic platform holds promise in accelerating the identification and design of anti-tumor compounds, a critical pursuit in advancing targeted cancer therapies.
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spelling pubmed-106660462023-11-09 An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer Chebanov, Dmitrii K. Misyurin, Vsevolod A. Shubina, Irina Zh. Front Bioinform Bioinformatics In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule anti-tumor agents. Our approach was exemplified in the context of combatting lung cancer. In the initial phase, target identification for therapeutic intervention was accomplished. Leveraging deep learning, we scrutinized gene expression profiles, focusing on those associated with adverse clinical outcomes in lung cancer patients. Augmenting this, generative adversarial neural (GAN) networks were employed to amass additional patient data. This effort yielded a subset of genes definitively linked to unfavorable prognoses. We further employed deep learning to delineate genes capable of discriminating between normal and tumor tissues based on expression patterns. The remaining genes were earmarked as potential targets for precision lung cancer therapy. Subsequently, a dedicated module was formulated to predict the interactions between inhibitors and proteins. To achieve this, protein amino acid sequences and chemical compound formulations engaged in protein interactions were encoded into vectorized representations. Additionally, a deep learning-based component was developed to forecast IC(50) values through experimentation on cell lines. Virtual pre-clinical trials employing these inhibitors facilitated the selection of pertinent cell lines for subsequent laboratory assays. In summary, our study culminated in the derivation of several small-molecule formulas projected to bind selectively to specific proteins. This algorithmic platform holds promise in accelerating the identification and design of anti-tumor compounds, a critical pursuit in advancing targeted cancer therapies. Frontiers Media S.A. 2023-11-09 /pmc/articles/PMC10666046/ /pubmed/38025397 http://dx.doi.org/10.3389/fbinf.2023.1225149 Text en Copyright © 2023 Chebanov, Misyurin and Shubina. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Chebanov, Dmitrii K.
Misyurin, Vsevolod A.
Shubina, Irina Zh.
An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title_full An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title_fullStr An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title_full_unstemmed An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title_short An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
title_sort algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666046/
https://www.ncbi.nlm.nih.gov/pubmed/38025397
http://dx.doi.org/10.3389/fbinf.2023.1225149
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