i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus
IntroductionDengue virus (DENV) is a significant global arboviral threat with fatal potential, currently lacking effective antiviral treatments or a universally applicable vaccine. In response to this unmet need, we developed the “i‐DENV” web server to facilitate structure‐based drug prediction targ...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1605722/full |
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| author | Sakshi Gautam Anamika Thakur Manoj Kumar |
| author_facet | Sakshi Gautam Anamika Thakur Manoj Kumar |
| author_sort | Sakshi Gautam |
| collection | DOAJ |
| description | IntroductionDengue virus (DENV) is a significant global arboviral threat with fatal potential, currently lacking effective antiviral treatments or a universally applicable vaccine. In response to this unmet need, we developed the “i‐DENV” web server to facilitate structure‐based drug prediction targeting key viral proteins.MethodsThe i‐DENV platform focuses on the NS3 protease and NS5 polymerase of DENV using machine learning techniques (MLTs) and quantitative structure‐activity relationship (QSAR) modeling. A total of 1213 and 157 unique compounds, along with their IC50 values targeting NS3 and NS5 respectively, were retrieved from the ChEMBL and DenvInD databases. Molecular descriptors and fingerprints were computed and used to train multiple regression‐based MLTs, including SVM, RF, kNN, ANN, XGBoost, and DNN, with ten‐fold cross‐validation.ResultsThe best-performing SVM and ANN models achieved Pearson correlation coefficients (PCCs) of 0.857/0.862 (NS3) and 0.982/0.964 (NS5) on training/testing sets, and 0.870/0.894 (NS3) and 0.970/0.977 (NS5) on independent validation sets. Model robustness was supported through scatter plots, chemical clustering, statistical analyses, decoy set etc. Virtual screening identified Micafungin, Oritavancin, and Iodixanol as top hits for NS2B/NS3 protease, and Cangrelor, Eravacycline, and Baloxavir marboxil for NS5 polymerase. Molecular docking further confirmed strong binding affinities of these compounds.DiscussionOur in-silico findings suggest these repurposed drugs as promising antiviral candidates against DENV. However, further in vitro and in vivo studies are essential to validate their therapeutic potential. The i-DENV web server is freely accessible at http://bioinfo.imtech.res.in/manojk/idenv/, offering a structure-specific drug prediction platform for DENV research and antiviral drug discovery. |
| format | Article |
| id | doaj-art-235154bdb8c04f44920e0913946a2ebc |
| institution | OA Journals |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-235154bdb8c04f44920e0913946a2ebc2025-08-20T02:24:14ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-06-011610.3389/fphar.2025.16057221605722i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virusSakshi GautamAnamika ThakurManoj KumarIntroductionDengue virus (DENV) is a significant global arboviral threat with fatal potential, currently lacking effective antiviral treatments or a universally applicable vaccine. In response to this unmet need, we developed the “i‐DENV” web server to facilitate structure‐based drug prediction targeting key viral proteins.MethodsThe i‐DENV platform focuses on the NS3 protease and NS5 polymerase of DENV using machine learning techniques (MLTs) and quantitative structure‐activity relationship (QSAR) modeling. A total of 1213 and 157 unique compounds, along with their IC50 values targeting NS3 and NS5 respectively, were retrieved from the ChEMBL and DenvInD databases. Molecular descriptors and fingerprints were computed and used to train multiple regression‐based MLTs, including SVM, RF, kNN, ANN, XGBoost, and DNN, with ten‐fold cross‐validation.ResultsThe best-performing SVM and ANN models achieved Pearson correlation coefficients (PCCs) of 0.857/0.862 (NS3) and 0.982/0.964 (NS5) on training/testing sets, and 0.870/0.894 (NS3) and 0.970/0.977 (NS5) on independent validation sets. Model robustness was supported through scatter plots, chemical clustering, statistical analyses, decoy set etc. Virtual screening identified Micafungin, Oritavancin, and Iodixanol as top hits for NS2B/NS3 protease, and Cangrelor, Eravacycline, and Baloxavir marboxil for NS5 polymerase. Molecular docking further confirmed strong binding affinities of these compounds.DiscussionOur in-silico findings suggest these repurposed drugs as promising antiviral candidates against DENV. However, further in vitro and in vivo studies are essential to validate their therapeutic potential. The i-DENV web server is freely accessible at http://bioinfo.imtech.res.in/manojk/idenv/, offering a structure-specific drug prediction platform for DENV research and antiviral drug discovery.https://www.frontiersin.org/articles/10.3389/fphar.2025.1605722/fullmachine learningantiviralsartificial intelligencealgorithmweb serverQSAR |
| spellingShingle | Sakshi Gautam Anamika Thakur Manoj Kumar i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus Frontiers in Pharmacology machine learning antivirals artificial intelligence algorithm web server QSAR |
| title | i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus |
| title_full | i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus |
| title_fullStr | i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus |
| title_full_unstemmed | i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus |
| title_short | i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus |
| title_sort | i denv development of qsar based regression models for predicting inhibitors targeting non structural ns proteins of dengue virus |
| topic | machine learning antivirals artificial intelligence algorithm web server QSAR |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1605722/full |
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