Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics

Lokesh Jiraka; Jeevan Karthik Madavareddi; Manaswini Cheruku; Srinivas Rao Maddi

doi:doi:10.11648/j.jddmc.20261202.11

Research Article |

| Peer-Reviewed

Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics

Lokesh Jiraka

, Jeevan Karthik Madavareddi

, Manaswini Cheruku

, Srinivas Rao Maddi^*

Published in Journal of Drug Design and Medicinal Chemistry (Volume 12, Issue 2)

Received: 27 June 2026 Accepted: 20 July 2026 Published: 10 August 2026

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Abstract

The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.

Published in	Journal of Drug Design and Medicinal Chemistry (Volume 12, Issue 2)
DOI	10.11648/j.jddmc.20261202.11
Page(s)	29-45
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Phytoconstituents, Dengue Fever, AI & ML Models, Insilico, Computer-aided Drug Discovery (CADD), Molecular Docking, Random Forest, Extreme Gradient Boosting

References

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Jiraka, L., Madavareddi, J. K., Cheruku, M., Maddi, S. R. (2026). Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. Journal of Drug Design and Medicinal Chemistry, 12(2), 29-45. https://doi.org/10.11648/j.jddmc.20261202.11

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ACS Style

Jiraka, L.; Madavareddi, J. K.; Cheruku, M.; Maddi, S. R. Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. J. Drug Des. Med. Chem. 2026, 12(2), 29-45. doi: 10.11648/j.jddmc.20261202.11

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AMA Style

Jiraka L, Madavareddi JK, Cheruku M, Maddi SR. Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. J Drug Des Med Chem. 2026;12(2):29-45. doi: 10.11648/j.jddmc.20261202.11

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@article{10.11648/j.jddmc.20261202.11,
  author = {Lokesh Jiraka and Jeevan Karthik Madavareddi and Manaswini Cheruku and Srinivas Rao Maddi},
  title = {Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics},
  journal = {Journal of Drug Design and Medicinal Chemistry},
  volume = {12},
  number = {2},
  pages = {29-45},
  doi = {10.11648/j.jddmc.20261202.11},
  url = {https://doi.org/10.11648/j.jddmc.20261202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jddmc.20261202.11},
  abstract = {The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.},
 year = {2026}
}

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TY  - JOUR
T1  - Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics
AU  - Lokesh Jiraka
AU  - Jeevan Karthik Madavareddi
AU  - Manaswini Cheruku
AU  - Srinivas Rao Maddi
Y1  - 2026/08/10
PY  - 2026
N1  - https://doi.org/10.11648/j.jddmc.20261202.11
DO  - 10.11648/j.jddmc.20261202.11
T2  - Journal of Drug Design and Medicinal Chemistry
JF  - Journal of Drug Design and Medicinal Chemistry
JO  - Journal of Drug Design and Medicinal Chemistry
SP  - 29
EP  - 45
PB  - Science Publishing Group
SN  - 2472-3576
UR  - https://doi.org/10.11648/j.jddmc.20261202.11
AB  - The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.
VL  - 12
IS  - 2
ER  -

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Lokesh Jiraka

Acubiosys Private Limited, Hyderabad, India

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http://orcid.org/0009-0002-4067-8142
Jeevan Karthik Madavareddi

Acubiosys Private Limited, Hyderabad, India

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http://orcid.org/0009-0009-4265-0814
Manaswini Cheruku

Acubiosys Private Limited, Hyderabad, India

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http://orcid.org/0009-0002-7817-5946
Srinivas Rao Maddi

Acubiosys Private Limited, Hyderabad, India

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http://orcid.org/0000-0002-7280-4900

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APA Style

Jiraka, L., Madavareddi, J. K., Cheruku, M., Maddi, S. R. (2026). Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. Journal of Drug Design and Medicinal Chemistry, 12(2), 29-45. https://doi.org/10.11648/j.jddmc.20261202.11

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ACS Style

Jiraka, L.; Madavareddi, J. K.; Cheruku, M.; Maddi, S. R. Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. J. Drug Des. Med. Chem. 2026, 12(2), 29-45. doi: 10.11648/j.jddmc.20261202.11

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AMA Style

Jiraka L, Madavareddi JK, Cheruku M, Maddi SR. Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics. J Drug Des Med Chem. 2026;12(2):29-45. doi: 10.11648/j.jddmc.20261202.11

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@article{10.11648/j.jddmc.20261202.11,
  author = {Lokesh Jiraka and Jeevan Karthik Madavareddi and Manaswini Cheruku and Srinivas Rao Maddi},
  title = {Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics},
  journal = {Journal of Drug Design and Medicinal Chemistry},
  volume = {12},
  number = {2},
  pages = {29-45},
  doi = {10.11648/j.jddmc.20261202.11},
  url = {https://doi.org/10.11648/j.jddmc.20261202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jddmc.20261202.11},
  abstract = {The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics
AU  - Lokesh Jiraka
AU  - Jeevan Karthik Madavareddi
AU  - Manaswini Cheruku
AU  - Srinivas Rao Maddi
Y1  - 2026/08/10
PY  - 2026
N1  - https://doi.org/10.11648/j.jddmc.20261202.11
DO  - 10.11648/j.jddmc.20261202.11
T2  - Journal of Drug Design and Medicinal Chemistry
JF  - Journal of Drug Design and Medicinal Chemistry
JO  - Journal of Drug Design and Medicinal Chemistry
SP  - 29
EP  - 45
PB  - Science Publishing Group
SN  - 2472-3576
UR  - https://doi.org/10.11648/j.jddmc.20261202.11
AB  - The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.
VL  - 12
IS  - 2
ER  -

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