Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach
Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs.Methods: In the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2024.1380266/full |
| _version_ | 1797254048732151808 |
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| author | Farhan Siddique Farhan Siddique Ahmar Anwaar Maryam Bashir Maryam Bashir Sumaira Nadeem Ravi Rawat Volkan Eyupoglu Samina Afzal Mehvish Bibi Yousef A. Bin Jardan Mohammed Bourhia |
| author_facet | Farhan Siddique Farhan Siddique Ahmar Anwaar Maryam Bashir Maryam Bashir Sumaira Nadeem Ravi Rawat Volkan Eyupoglu Samina Afzal Mehvish Bibi Yousef A. Bin Jardan Mohammed Bourhia |
| author_sort | Farhan Siddique |
| collection | DOAJ |
| description | Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs.Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively.Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis.Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches. |
| first_indexed | 2024-04-24T21:43:46Z |
| format | Article |
| id | doaj.art-82b0e7cf9a8a4a4cbe90a23cb420e3f8 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-04-24T21:43:46Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-82b0e7cf9a8a4a4cbe90a23cb420e3f82024-03-21T05:12:30ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462024-03-011210.3389/fchem.2024.13802661380266Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approachFarhan Siddique0Farhan Siddique1Ahmar Anwaar2Maryam Bashir3Maryam Bashir4Sumaira Nadeem5Ravi Rawat6Volkan Eyupoglu7Samina Afzal8Mehvish Bibi9Yousef A. Bin Jardan10Mohammed Bourhia11School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, ChinaDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, PakistanFaculty of Pharmacy, Bahauddin Zakariya University, Multan, PakistanDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, PakistanSouthern Punjab Institute of Health Sciences, Multan, PakistanDepartment of Pharmacy, The Women University, Multan, PakistanSchool of Health Sciences & Technology, UPES University, Dehradun, IndiaDepartment of Chemistry, Cankırı Karatekin University, Cankırı, TürkiyeDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, PakistanDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, PakistanDepartment of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi ArabiaLaboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, MoroccoIntroduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs.Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively.Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis.Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.https://www.frontiersin.org/articles/10.3389/fchem.2024.1380266/fulldeep learningQSARvirtual screeningdensity function theorymolecular dynamicsdihydrofolate reductase |
| spellingShingle | Farhan Siddique Farhan Siddique Ahmar Anwaar Maryam Bashir Maryam Bashir Sumaira Nadeem Ravi Rawat Volkan Eyupoglu Samina Afzal Mehvish Bibi Yousef A. Bin Jardan Mohammed Bourhia Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach Frontiers in Chemistry deep learning QSAR virtual screening density function theory molecular dynamics dihydrofolate reductase |
| title | Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach |
| title_full | Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach |
| title_fullStr | Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach |
| title_full_unstemmed | Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach |
| title_short | Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach |
| title_sort | revisiting methotrexate and phototrexate zinc15 library based derivatives using deep learning in silico drug design approach |
| topic | deep learning QSAR virtual screening density function theory molecular dynamics dihydrofolate reductase |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2024.1380266/full |
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