Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus
Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major con...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/9/2729 |
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| author | Wissal Liman Mehdi Oubahmane Ismail Hdoufane Imane Bjij Didier Villemin Rachid Daoud Driss Cherqaoui Achraf El Allali |
| author_facet | Wissal Liman Mehdi Oubahmane Ismail Hdoufane Imane Bjij Didier Villemin Rachid Daoud Driss Cherqaoui Achraf El Allali |
| author_sort | Wissal Liman |
| collection | DOAJ |
| description | Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R<sup>2</sup> = 0.991 and Q<sup>2</sup> = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R<sup>2</sup> = 0.915 and Q<sup>2</sup> = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV. |
| first_indexed | 2024-03-10T03:54:57Z |
| format | Article |
| id | doaj.art-ba6f670285f049dea382b78be99b8e65 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T03:54:57Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-ba6f670285f049dea382b78be99b8e652023-11-23T08:48:45ZengMDPI AGMolecules1420-30492022-04-01279272910.3390/molecules27092729Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C VirusWissal Liman0Mehdi Oubahmane1Ismail Hdoufane2Imane Bjij3Didier Villemin4Rachid Daoud5Driss Cherqaoui6Achraf El Allali7African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, MoroccoDepartment of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, MoroccoDepartment of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, MoroccoInstitut Supérieur des Professions Infirmières et Techniques de Santé (ISPITS), Dakhla 73000, MoroccoEcole Nationale Supérieure d’Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique, UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, 14118 Caen, FranceAfrican Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, MoroccoDepartment of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, MoroccoAfrican Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, MoroccoHepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R<sup>2</sup> = 0.991 and Q<sup>2</sup> = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R<sup>2</sup> = 0.915 and Q<sup>2</sup> = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.https://www.mdpi.com/1420-3049/27/9/2729chemoinformaticsdrug discoverymolecular descriptorsQSARHCVNS5A |
| spellingShingle | Wissal Liman Mehdi Oubahmane Ismail Hdoufane Imane Bjij Didier Villemin Rachid Daoud Driss Cherqaoui Achraf El Allali Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus Molecules chemoinformatics drug discovery molecular descriptors QSAR HCV NS5A |
| title | Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus |
| title_full | Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus |
| title_fullStr | Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus |
| title_full_unstemmed | Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus |
| title_short | Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus |
| title_sort | monte carlo method and ga mlr based qsar modeling of ns5a inhibitors against the hepatitis c virus |
| topic | chemoinformatics drug discovery molecular descriptors QSAR HCV NS5A |
| url | https://www.mdpi.com/1420-3049/27/9/2729 |
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