Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Farhan Siddique; Ahmar Anwaar; Maryam Bashir; Sumaira Nadeem; Ravi Rawat; Volkan Eyupoglu; Samina Afzal; Mehvish Bibi; Yousef A Bin Jardan; Mohammed Bourhia

doi:10.3389/fchem.2024.1380266

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Front Chem. 2024 Mar 21:12:1380266. doi: 10.3389/fchem.2024.1380266. eCollection 2024.

Authors

Farhan Siddique^{1

2}, Ahmar Anwaar³, Maryam Bashir^{2

4}, Sumaira Nadeem⁵, Ravi Rawat⁶, Volkan Eyupoglu⁷, Samina Afzal², Mehvish Bibi², Yousef A Bin Jardan⁸, Mohammed Bourhia⁹

Affiliations

¹ School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
² Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan.
³ Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan.
⁴ Southern Punjab Institute of Health Sciences, Multan, Pakistan.
⁵ Department of Pharmacy, The Women University, Multan, Pakistan.
⁶ School of Health Sciences & Technology, UPES University, Dehradun, India.
⁷ Department of Chemistry, Cankırı Karatekin University, Cankırı, Türkiye.
⁸ Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
⁹ Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco.

Abstract

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.

Keywords: QSAR; deep learning; density function theory; dihydrofolate reductase; molecular dynamics; virtual screening.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors would like to extend their sincere appreciation to the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia for funding this work through the project number (RSP2024R457).