210
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

, , , , &
Received 25 Jul 2023, Accepted 04 Dec 2023, Published online: 23 Dec 2023

References

  • Abe, Y., Odaka, M., Inagaki, F., Lax, I., Schlessinger, J., & Kohda, D. (1998). Disulfide bond structure of human epidermal growth factor receptor. The Journal of Biological Chemistry, 273(18), 11150–11157. https://doi.org/10.1074/jbc.273.18.11150
  • Abed, S. S., P, K., Imran, K., & Lateef, S. S. (2023). Gas chromatography-mass spectrometry (GC-MS) metabolite profiling of Citrus limon (L.) Osbeck juice extract evaluated for its antimicrobial activity against Streptococcus mutans. Cureus, 15(1), e33585. https://doi.org/10.7759/cureus.33585
  • Acharya, R., Chacko, S., Bose, P., Lapenna, A., & Pattanayak, S. P. (2019). Structure based multitargeted molecular docking analysis of selected furanocoumarins against breast cancer. Scientific Reports, 9(1), 15743. https://doi.org/10.1038/s41598-019-52162-0
  • Ahmad, I., Kumar, D., & Patel, H. (2022). Computational investigation of phytochemicals from Withania somnifera (Indian ginseng/ashwagandha) as plausible inhibitors of GluN2B-containing NMDA receptors. Journal of Biomolecular Structure & Dynamics, 40(17), 7991–8003. https://doi.org/10.1080/07391102.2021.1905553
  • Aier, I., Varadwaj, P. K., & Raj, U. (2016). Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Scientific Reports, 6, 34984. https://doi.org/10.1038/srep34984
  • AlAsmari, A. F., Ali, N., AlAsmari, F., AlAnazi, W. A., AlShammari, M. A., Al-Harbi, N. O., Alhoshani, A., As Sobeai, H. M., AlSwayyed, M., AlAnazi, M. M., & AlGhamdi, N. S. (2020). Liraglutide attenuates gefitinib-induced cardiotoxicity and promotes cardioprotection through the regulation of MAPK/NF-κB signaling pathways. Saudi Pharmaceutical Journal, 28(4), 509–518. https://doi.org/10.1016/j.jsps.2020.03.002
  • Ali, R., & Wendt, M. K. (2017). The paradoxical functions of EGFR during breast cancer progression. Signal Transduction and Targeted Therapy, 2(1), 1–7. https://doi.org/10.1038/sigtrans.2016.42
  • Amelia, T., Kartasasmita, R. E., Ohwada, T., & Tjahjono, D. H. (2022). Structural insight and development of EGFR tyrosine kinase inhibitors. Molecules, 27(3), 819. https://doi.org/10.3390/molecules27030819
  • Anbuselvam, M., Easwaran, M., Meyyazhagan, A., Anbuselvam, J., Bhotla, H. K., Sivasubramanian, M., Annadurai, Y., Kaul, T., Pappusamy, M., & Balasubramanian, B. (2021). Structure-based virtual screening, pharmacokinetic prediction, molecular dynamics studies for the identification of novel EGFR inhibitors in breast cancer. Journal of Biomolecular Structure & Dynamics, 39(12), 4462–4471. https://doi.org/10.1080/07391102.2020.1777899
  • Anitha, R., & Kannan, P. (2006). Antifungal activity of Clerodendrum inerme (L). and Clerodendrum phlomidis (L). Turkish Journal of Biology, 30(3), 139–142.
  • Anupama, N., Madhumitha, G., & Rajesh, K. S. (2014). Role of dried fruits of carissa carandas as anti-inflammatory agents and the analysis of phytochemical constituents by GC-MS. Biomed Research International, 2014, 1–6. https://doi.org/10.1155/2014/512369
  • Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., Vignat, J., Gralow, J. R., Cardoso, F., Siesling, S., & Soerjomataram, I. (2022). Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast, 66(August), 15–23. https://doi.org/10.1016/j.breast.2022.08.010
  • Ba Vinh, L., Thi Minh Nguyet, N., Young Yang, S., Hoon Kim, J., Thi Vien, L., Thi Thanh Huong, P., Van Thanh, N., Xuan Cuong, N., Hoai Nam, N., Van Minh, C., Hwang, I., & Ho Kim, Y. (2017). A new rearranged abietane diterpene from Clerodendrum inerme with antioxidant and cytotoxic activities. Natural Product Research, 32(17), 2001–2007. https://doi.org/10.1080/14786419.2017.1360885
  • Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
  • Bauer, J. A., Pavlović, J., & Bauerová-Hlinková, V. (2019). Normal mode analysis as a routine part of a structural investigation. Molecules, 24(18), 3293. https://doi.org/10.3390/molecules24183293
  • Becke, A. D. (1988). Density-functional exchange-energy approximation with correct asymptotic behavior. Physical Review. A, General Physics, 38(6), 3098–3100. https://doi.org/10.1103/physreva.38.3098
  • Ben Mahmoud, C., Anelli, A., Csányi, G., & Ceriotti, M. (2020). Learning the electronic density of states in condensed matter. Physical Review B, 102(23), 1–14. https://doi.org/10.1103/PhysRevB.102.235130
  • Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the rule of 5 and drugability. Advanced Drug Delivery Reviews, 101(1), 89–98. https://doi.org/10.1016/j.addr.2016.05.007
  • Bhattacharya, K., Bordoloi, R., Chanu, N. R., Kalita, R., Sahariah, B. J., & Bhattacharjee, A. (20, 2022). In silico discovery of 3 novel quercetin derivatives against papain-like protease, spike protein, and 3C-like protease of SARS-CoV-2. Journal, Genetic Engineering & Biotechnology, 20(1), 43. https://doi.org/10.1186/s43141-022-00314-7
  • Borrero-García, L. D., Del Mar Maldonado, M., Medina-Velázquez, J., Troche-Torres, A. L., Velazquez, L., Grafals-Ruiz, N., & Dharmawardhane, S. (2021). Rac inhibition as a novel therapeutic strategy for EGFR/HER2 targeted therapy resistant breast cancer. BMC Cancer, 21(1), 652. https://doi.org/10.1186/s12885-021-08366-7
  • Bultum, L. E., Tolossa, G. B., Kim, G., Kwon, O., & Lee, D. (2022). In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models. Scientific Reports, 12(1), 22221. https://doi.org/10.1038/s41598-022-26446-x
  • Casalvieri, K. A., Matheson, C. J., Backos, D. S., & Reigan, P. (2020). Molecular docking of substituted pteridinones and pyrimidines to the ATP-binding site of the N- terminal domain of RSK2 and associated MM/GBSA and molecular fi eld datasets. Data in Brief, 29, 105347. https://doi.org/10.1016/j.dib.2020.105347
  • Chen, C. P., Chen, C. C., Huang, C. W., & Chang, Y. C. (2018). Evaluating molecular properties involved in transport of small molecules in stratum corneum: A quantitative structure-activity relationship for skin permeability. Molecules, 23(4), 1–17. https://doi.org/10.3390/molecules23040911
  • Chen, W., Hou, J., Yin, Y., Jang, J., Zheng, Z., Fan, H., & Zou, G. (2010). α-Bisabolol induces dose- and time-dependent apoptosis in HepG2 cells via a Fas- and mitochondrial-related pathway, involves p53 and NFκB. Biochemical Pharmacology, 80(2), 247–254. https://doi.org/10.1016/j.bcp.2010.03.021
  • Choudhari, A. S., Mandave, P. C., Deshpande, M., Ranjekar, P., & Prakash, O. (2019). Phytochemicals in cancer treatment: From preclinical studies to clinical practice. Frontiers in Pharmacology, 10(January), 1614. https://doi.org/10.3389/fphar.2019.01614
  • Chouhan, M. K., Hurakadle, P. J., & Hegde, H. V. (2018a). Clerodendrum inerme (L.) Gaertn. extract exerts anticancer activity on lung cancer cells. Dhaka University Journal of Pharmaceutical Sciences, 17(2), 191–196. https://doi.org/10.3329/dujps.v17i2.39175
  • Chouhan, M. K., Hurkadale, P. J., & Hegde, H. V. (2018b). Evaluation of Clerodendrum inerme (L.) Gaertn. on Burkitt’s lymphoma cancer. Indian Journal of Pharmaceutical Education and Research, 52(2), 241–247. https://doi.org/10.5530/ijper.52.2.27
  • Costarelli, L., Malavolta, M., Giacconi, R., Cipriano, C., Gasparini, N., Tesei, S., Pierpaoli, S., Orlando, F., Suzuki, H., Perbellini, L., Piacenza, F., Emanuelli, M., & Mocchegiani, E. (2010). In vivo effect of α-bisabolol, a nontoxic sesquiterpene alcohol, on the induction of spontaneous mammary tumors in HER-2/neu transgenic mice. Oncology Research, 18(9), 409–418. https://doi.org/10.3727/096504010X12671222663557
  • Cragg, G. M., & Pezzuto, J. M. (2016). Natural products as a vital source for the discovery of cancer chemotherapeutic and chemopreventive agents. Medical Principles and Practice, 25 Suppl 2(Suppl 2), 41–59. https://doi.org/10.1159/000443404
  • Creanza, T. M., Delre, P., Ancona, N., Lentini, G., Saviano, M., & Mangiatordi, G. F. (2021). Structure-based prediction of hERG-related cardiotoxicity: A benchmark study. Journal of Chemical Information and Modeling, 61(9), 4758–4770. https://doi.org/10.1021/acs.jcim.1c00744
  • D. E. Shaw Research (2020). Schrödinger Release 2020-3: Desmond molecular dynamics system. Maestro-Desmond Interoperability Tools, Schrödinger.
  • Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 42717. https://doi.org/10.1038/srep42717
  • Dasmahapatra, U., Kumar, C. K., Das, S., Subramanian, P. T., Murali, P., Isaac, A. E., Ramanathan, K., Mm, B., & Chanda, K. (2022). In-silico molecular modelling, MM/GBSA binding free energy and molecular dynamics simulation study of novel pyrido fused imidazo[4,5-c]quinolines as potential anti-tumor agents. Frontiers in Chemistry, 10, 991369. https://doi.org/10.3389/fchem.2022.991369
  • Dassault Systèmes (2023). BIOVIA, Dassault Systèmes: BIOVIA Discovery Studio Visualizer. Dassault Systèmes [Online]. Retrieved from https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/biovia-discovery-studio/visualization/
  • Deepasree, K., & Venugopal, S. (2023). Molecular docking and dynamic simulation studies of terpenoid compounds against phosphatidylinositol-specific phospholipase C from Listeria monocytogenes. Informatics in Medicine Unlocked, 39(March), 101252. https://doi.org/10.1016/j.imu.2023.101252
  • Deghady, A. M., Hussein, R. K., Alhamzani, A. G., & Mera, A. (2021). Density functional theory and molecular docking investigations of the chemical and antibacterial activities for 1-(4-hydroxyphenyl)-3-phenylprop-2-en-1-one. Molecules, 26(12), 3631. https://doi.org/10.3390/molecules26123631
  • Eddin, L. B., Jha, N. K., Goyal, S. N., Agrawal, Y. O., Subramanya, S. B., Bastaki, S. M. A., & Ojha, S. (2022). Health benefits, pharmacological effects, molecular mechanisms, and therapeutic potential of α-Bisabolol. Nutrients, 14(7), 1370. https://doi.org/10.3390/nu14071370
  • Ejiofor, E. U., Ishebe, J. E., Benjamin, I., Okon, G. A., Gber, T. E., & Louis, H. (2023). Exploring the potential of single-metals (Cu, Ni, Zn) decorated Al12N12 nanostructures as sensors for flutamide anticancer drug. Heliyon, 9(10), e20682. https://doi.org/10.1016/j.heliyon.2023.e20682
  • Ejiofor, I. I. (2023). Computational phytochemistry, databases, and tools. In M. Rudrapal & H. Tijjani (Eds.), Phytochemistry, computational tools and databases in drug discovery: In Drug discovery update, Chukwuebuka Egbuna (1st ed., pp. 39–55). Elsevier Inc. https://doi.org/10.1016/C2020-0-03972-1
  • El-Saady, A. A., Roushdy, N., Farag, A. A. M., El-Nahass, M. M., & Abdel Basset, D. M. (2023). Exploring the molecular spectroscopic and electronic characterization of nanocrystalline Metal-free phthalocyanine: A DFT investigation. Optical and Quantum Electronics, 55, 662. https://doi.org/10.1007/s11082-023-04877-8
  • Fathollahi, M., Fathollahi, A., Motamedi, H., Moradi, J., Alvandi, A., & Abiri, R. (2021). In silico vaccine design and epitope mapping of New Delhi metallo-beta-lactamase (NDM): An immunoinformatics approach. BMC Bioinformatics, 22(1), 458. https://doi.org/10.1186/s12859-021-04378-z
  • Feinstein, W. P., & Brylinski, M. (2015). Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. Journal of Cheminformatics, 7(1), 18. https://doi.org/10.16/s13321-015-0067-5
  • Gavrilas, L. I., Cruceriu, D., Mocan, A., Loghin, F., Miere, D., & Balacescu, O. (2022). Plant-derived bioactive compounds in colorectal cancer: Insights from combined regimens with conventional chemotherapy to overcome drug-resistance. Biomedicines, 10(8), 1948. https://doi.org/10.3390/biomedicines10081948
  • Gong, Z., Liu, Z., Dong, X., Ding, Y.-H., Dong, M.-Q., & Tang, C. (2017). Protocol for analyzing protein ensemble structures from chemical cross-links using DynaXL. Biophysics Reports, 3(4), 100–108. https://doi.org/10.1007/s41048-017-0044-9
  • Gopal, N., & Sengottuvelu, S. (2008). Hepatoprotective activity of Clerodendrum inerme against CCL4 induced hepatic injury in rats. Fitoterapia, 79(1), 24–26. https://doi.org/10.1016/j.fitote.2007.07.006
  • Gopalakrishnan, S. B., Kalaiarasi, T., & Subramanian, R. (2014). Comparative DFT study of phytochemical constituents of the fruits of Cucumis trigonus Roxb. and Cucumis sativus Linn. Journal of Computational Physics, 2014, 1–6. https://doi.org/10.1155/2014/623235
  • Grijaldo, S. J. B., Alvarez, M. R. S., Heralde, F. M., Nacario, R. C., Lebrilla, C. B., Rabajante, J. F., & Completo, G. C. (2023). Integrating computational methods in network pharmacology and in silico screening to uncover multi-targeting phytochemicals against aberrant protein glycosylation in lung cancer. ACS Omega, 8(23), 20303–20312. https://doi.org/10.1021/acsomega.2c07542
  • Guan, L., Yang, H., Cai, Y., Sun, L., Di, P., Li, W., Liu, G., & Tang, Y. (2018). ADMET-score-a comprehensive scoring function for evaluation of chemical drug-likeness. Medchemcomm, 10(1), 148–157. https://doi.org/10.1039/C8MD00472B
  • Gulati, P., Chadha, J., Harjai, K., & Singh, S. (2022). Targeting envelope proteins of poxviruses to repurpose phytochemicals against monkeypox: An in silico investigation. Frontiers in Microbiology, 13, 1073419. https://doi.org/10.3389/fmicb.2022.1073419
  • Han, Y., Zhang, J., Hu, C. Q., Zhang, X., Ma, B., & Zhang, P. (2019). In silico ADME and toxicity prediction of ceftazidime and its impurities. Frontiers in Pharmacology, 10, 434. https://doi.org/10.3389/fphar.2019.00434
  • Hazarika, Z., & Jha, A. N. (2020). Computational analysis of the silver nanoparticle – human serum albumin complex. ACS Omega, 5(1), 170–178. https://doi.org/10.1021/acsomega.9b02340
  • He, Y., Si, Y., Li, X., Hong, J., Yu, C., & He, N. (2022). The relationship between tobacco and breast cancer incidence: A systematic review and meta-analysis of observational studies. Frontiers in Oncology, 12, 961970. https://doi.org/10.3389/fonc.2022.961970
  • Hollingsworth, S. A., Dror, R. O., Physiology, C., & Engineering, M. (2019). Molecular dynamics simulation for all. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
  • Ibrahim, M. T., Uzairu, A., Shallangwa, G. A., & Uba, S. (2020). Structure-based design of some quinazoline derivatives as epidermal growth factor receptor inhibitors. Egyptian Journal of Medical Human Genetics, 21(63), 1–12. https://doi.org/10.1186/s43042-020-00107-y
  • Ibrahim, S. R. M., Alshali, K. Z., Fouad, M. A., Elkhayat, E. S., Al Haidari, R. A., & Mohamed, G. A. (2014). Chemical constituents and biological investigations of the aerial parts of Egyptian Clerodendrum inerme. Bulletin of Faculty of Pharmacy, Cairo University, 52(2), 165–170. https://doi.org/10.1016/j.bfopcu.2014.05.002
  • Jiang, D., Ye, Z., Hsieh, C.-Y., Yang, Z., Zhang, X., Kang, Y., Du, H., Wu, Z., Wang, J., Zeng, Y., Zhang, H., Wang, X., Wang, M., Yao, X., Zhang, S., Wu, J., & Hou, T. (2023). MetalProGNet: A structure-based deep graph model for metalloprotein-ligand interaction predictions. Chemical Science, 14(8), 2054–2069. https://doi.org/10.1039/d2sc06576b
  • Kalavathi, R., & Sagayagiri, R. (2016). Anticancer activity of ethanolic leaf extract of Clerodendrum inerme against lung adenocarcinoma epithelial cell line. European Journal of Molecular Biology and Biochemistry, 3(2), 69–72.
  • Kastenholz, M. A., Pastor, M., Cruciani, G., Haaksma, E. E. J., & Fox, T. (2000). GRID/CPCA: A new computational tool to design selective ligands. Journal of Medicinal Chemistry, 43(16), 3033–3044. https://doi.org/10.1021/jm000934y
  • Kato, H. (2020). Computational prediction of cytochrome P450 inhibition and induction. Drug Metabolism and Pharmacokinetics, 35(1), 30–44. https://doi.org/10.1016/j.dmpk.2019.11.006
  • Khan, S. A., Kanwal, S., Rizwan, K., & Shahid, S. (2018). Enhanced antimicrobial, antioxidant, in vivo antitumor and in vitro anticancer effects against breast cancer cell line by green synthesized un-doped SnO2 and Co-doped SnO2 nanoparticles from Clerodendrum inerme. Microbial Pathogenesis, 125, 366–384. https://doi.org/10.1016/j.micpath.2018.09.041
  • Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395. https://doi.org/10.1093/nar/gkaa971
  • Koepsell, H. (2021). Update on drug-drug interaction at organic cation transporters: Mechanisms, clinical impact, and proposal for advanced in vitro testing. Expert Opinion on Drug Metabolism & Toxicology, 17(6), 635–653. https://doi.org/10.1080/17425255.2021.1915284
  • Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D. A., & Cheatham, T. E. (2000). Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts of Chemical Research, 33(12), 889–897. https://doi.org/10.1021/ar000033j
  • Kovacs, E., Zorn, J. A., Huang, Y., Barros, T., & Kuriyan, J. (2015). A structural perspective on the regulation of the epidermal growth factor receptor. Annual Review of Biochemistry, 84(1), 739–764. https://doi.org/10.1146/annurev-biochem-060614-034402
  • Kovacs, J. A., Chacón, P., & Abagyan, R. (2004). Predictions of protein flexibility: First-order measures. Proteins: Structure, Function, and Bioinformatics, 56(4), 661–668. https://doi.org/10.1002/prot.20151
  • Kumar, S., Abbas, F., Ali, I., Gupta, M. K., Kumar, S., Garg, M., & Kumar, D. (2023). Integrated network pharmacology and in-silico approaches to decipher the pharmacological mechanism of Selaginella tamariscina in the treatment of non-small cell lung cancer. Phytomedicine Plus, 3(2), 100419. https://doi.org/10.1016/j.phyplu.2023.100419
  • Lagorce, D., Douguet, D., Miteva, M. A., & Villoutreix, B. O. (2017). Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Scientific Reports, 7(1), 46277. https://doi.org/10.1038/srep46277
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23(1–3), 3–25. https://doi.org/10.1016/S0169-409X(96)00423-1
  • Liu, C. T., Zhang, M., Yan, P., Liu, H. C., Liu, X. Y., & Zhan, R. T. (2016). Qualitative and quantitative analysis of volatile components of Zhengtian pills using gas chromatography mass spectrometry and ultra-high performance liquid chromatography. Journal of Analytical Methods in Chemistry, 2016, 1–8. https://doi.org/10.1155/2016/1206391
  • Liu, W., Liu, Z., Liu, H., Westerhoff, L. M., & Zheng, Z. (2022). Free energy calculations using the movable type method with molecular dynamics driven protein-ligand sampling. Journal of Chemical Information and Modeling, 62(22), 5645–5665. https://doi.org/10.1021/acs.jcim.2c00278
  • López-Blanco, J. R., Aliaga, J. I., Quintana-Ortí, E. S., & Chacón, P. (2014). iMODS : Internal coordinates normal mode analysis server. Nucleic Acids Research, 42(Web Server issue), W271–W276. https://doi.org/10.1093/nar/gku339
  • Luo, P., Yan, H., Du, J., Chen, X., Shao, J., Zhang, Y., Xu, Z., Jin, Y., Lin, N., Yang, B., & He, Q. (2021). PLK1 (polo like kinase 1)-dependent autophagy facilitates gefitinib-induced hepatotoxicity by degrading COX6A1 (cytochrome c oxidase subunit 6A1). Autophagy, 17(10), 3221–3237. https://doi.org/10.1080/15548627.2020.1851492
  • Magyar, C., Németh, B. Z., Cserző, M., & Simon, I. (2023). Molecular dynamics simulation as a tool to identify mutual synergistic folding proteins. International Journal of Molecular Sciences, 24(2), 1790. https://doi.org/10.3390/ijms24021790
  • Majeed, A., Hussain, W., Yasmin, F., Akhtar, A., & Rasool, N. (2021). Virtual screening of phytochemicals by targeting HR1 domain of SARS-CoV-2 S protein: Molecular docking, molecular dynamics simulations, and DFT studies. Biomed Research International, 2021, 1–19. https://doi.org/10.1155/2021/6661191
  • Malik, F. K., & Tao Guo, J. (2022). Insights into protein–DNA interactions from hydrogen bond energy-based comparative protein–ligand analyses. Proteins: Structure, Function, and Bioinformatics, 90(6), 1303–1314. https://doi.org/10.1002/prot.26313
  • Manjulatha, K., Gul, M. Z., Imam, N., Ghazi, I. A., & Setty, O. H. (2016). Phytochemical content and antioxidant potential of Clerodendrum inerme and its different parts-A comparative study. Journal of Biologically Active Products from Nature, 6(1), 65–77. https://doi.org/10.1080/22311866.2016.1162111
  • Manoharan, S., Kavitha, K., Senthil, N., & Renju, G. L. (2006). Evaluation of anticarcinogenic effects of Clerodendron inerme on 7,12-dimethylbenz(a) anthracene-induced hamster buccal pouch carcinogenesis. Singapore Medical Journal, 47(12), 1038–1043.
  • Marikkannu, K. K. A., & Ganesan, S. (2021). Molecular docking and GC-MS data for the inhibition of RAD51 expression by a compound from Clerodendrum inerme L. Bioinformation, 17(8), 767–771. https://doi.org/10.6026/97320630017767
  • Masuda, H., Zhang, D., Bartholomeusz, C., Doihara, H., Hortobagyi, G. N., & Ueno, N. T. (2012). Role of epidermal growth factor receptor in breast cancer. Breast Cancer Research and Treatment, 136(2), 331–345. https://doi.org/10.1007/s10549-012-2289-9
  • McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., & Koes, D. R. (2021). GNINA 1.0: Molecular docking with deep learning. Journal of Cheminformatics, 13(1), 43. https://doi.org/10.1186/s13321-021-00522-2
  • Mendes, F. B., Bergamin, L. S., Dos Santos Stuepp, C., Braganhol, E., Terroso, T., Pohlmann, A. R., Guterres, S. S., & Battastini, A. M. O. (2017). Alpha-bisabolol promotes glioma cell death by modulating the adenosinergic system. Anticancer Research, 37(4), 1819–1823. https://doi.org/10.21873/anticanres.11516
  • Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011). Molecular docking: A powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146–157. https://doi.org/10.47583/ijpsrr.2022.v77i02.029
  • Mirzadeh, A., Kobakhidze, G., Vuillemot, R., Jonic, S., & Rouiller, I. (2022). In silico prediction, characterization, docking studies and molecular dynamics simulation of human p97 in complex with p37 cofactor. BMC Molecular and Cell Biology, 23(1), 1–12. https://doi.org/10.1186/s12860-022-00437-2
  • Mohs, R. C., & Greig, N. H. (2017). Drug discovery and development: Role of basic biological research. Alzheimer’s & Dementia, 3(4), 651–657. https://doi.org/10.1016/j.trci.2017.10.005
  • Mollazadeh, S., Hadizadeh, F., & Ferreira, R. J. (2021). Theoretical studies on 1,4-dihydropyridine derivatives as P-glycoprotein allosteric inhibitors: Insights on symmetry and stereochemistry. Journal of Biomolecular Structure & Dynamics, 39(13), 4752–4763. https://doi.org/10.1080/07391102.2020.1780942
  • Morris, G. M., Huey, R., & Olson, A. J. (2008). Using AutoDock for ligand-receptor docking. Current Protocols in Bioinformatics. 2008;Chapter 8:Unit 8.14. https://doi.org/10.1002/0471250953.bi0814s24
  • Murad, N., Pasikanti, K. K., Madej, B. D., Minnich, A., McComas, J. M., Crouch, S., Polli, J. W., & Weber, A. D. (2021). Predicting volume of distribution in humans: Performance of in silico methods for a large set of structurally diverse clinical compounds. Drug Metabolism and Disposition: The Biological Fate of Chemicals, 49(2), 169–178. https://doi.org/10.1124/DMD.120.000202.
  • Nath, A., Kumer, A., Zaben, F., & Khan, M. W. (2021). Investigating the binding affinity, molecular dynamics, and ADMET properties of 2,3-dihydrobenzofuran derivatives as an inhibitor of fungi, bacteria, and virus protein. Beni-Suef University Journal of Basic and Applied Sciences, 10(1), 1–13. https://doi.org/10.1186/s43088-021-00117-8
  • Nishinarizki, V., Hardianto, A., Gaffar, S., Muchtaridi, M., & Herlina, T. (2022). Virtual screening campaigns and ADMET evaluation to unlock the potency of flavonoids from Erythrina as 3CLpro SARS-COV-2 inhibitors. Journal of Applied Pharmaceutical Science, 13(2), 78–88. https://doi.org/10.7324/JAPS.2023.130209
  • Nittinger, E., Inhester, T., Bietz, S., Meyder, A., Schomburg, K. T., Lange, G., Klein, R., & Rarey, M. (2017). Large-scale analysis of hydrogen bond interaction patterns in protein-ligand interfaces. Journal of Medicinal Chemistry, 60(10), 4245–4257. https://doi.org/10.1021/acs.jmedchem.7b00101
  • Noori, S., Hassan, Z. M., Yaghmaei, B., & Dolatkhah, M. (2013). Antitumor and immunomodulatory effects of salvigenin on tumor bearing mice. Cellular Immunology, 286(1–2), 16–21. https://doi.org/10.1016/j.cellimm.2013.10.005
  • O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel. J. Cheminform, 3(33), 1–14. Retrieved from 10.1186/1758-2946-3-33
  • O'Boyle, N. M., Tenderholt, A. L., & Langner, K. M. (2007). cclib: A library for package-independent computational chemistry algorithms. Journal of Computational Chemistry, 29(5), 839–845. https://doi.org/10.1002/jcc.20823
  • Owoloye, A. J., Ligali, F. C., Enejoh, O. A., Musa, A. Z., Aina, O., Idowu, E. T., & Oyebola, K. M. (2022). Molecular docking, simulation and binding free energy analysis of small molecules as Pf HT1 inhibitors. PLOS One, 17(8), e0268269. https://doi.org/10.1371/journal.pone.0268269
  • Patel, K., Patel, J., Patel, M., Rajput, G., & Patel, H. (2010). Introduction to hyphenated techniques and their applications in pharmacy. Pharmaceutical Methods, 1(1), 2–13. https://doi.org/10.4103/2229-4708.72222
  • Patel, P. P., Patel, N. B., Tople, M. S., Patel, V. M., Ahmed, I., & Patel, H. (2023). Microwave produced 8-methyl-1,2,4,8-tetraazaspiro[4.5]dec-2-en-3-amine derivatives: Their in vitro and in silico analysis. Molecular Diversity, 27, 1–14. https://doi.org/10.1007/s11030-023-10665-z
  • Patil, P. B., Kallapur, S. V., Kallapur, V. L., & Holihosur, S. N. (2014). Clerodendron inerme Gaertn. plant as an effective natural product against dengue and filarial vector mosquitoes. Asian Pacific Journal of Tropical Disease, 4(S1), S453–S462. https://doi.org/10.1016/S2222-1808(14)60490-4
  • Patil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., & Varma, A. K. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLOS One, 5(8), e12029. https://doi.org/10.1371/journal.pone.0012029
  • Pawara, R., Ahmad, I., Surana, S., & Patel, H. (2021). Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation. In Silico Pharmacology, 9(1), 54. https://doi.org/10.1007/s40203-021-00113-x
  • Pham The, H., González-Álvarez, I., Bermejo, M., Mangas Sanjuan, V., Centelles, I., Garrigues, T. M., & Cabrera-Pérez, M. Á. (2011). In silico prediction of caco-2 cell permeability by a classification QSAR approach. Molecular Informatics, 30(4), 376–385. https://doi.org/10.1002/minf.201000118
  • Phosrithong, N., & Nuchtavorn, N. (2016). Antioxidant and anti-inflammatory activites of Clerodendrum leaf extracts collected in Thailand. European Journal of Integrative Medicine, 8(3), 281–285. https://doi.org/10.1016/j.eujim.2015.10.002
  • Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
  • Prabhakaran, P., Hebbani, A. V., Menon, S. V., Paital, B., Murmu, S., Kumar, S., Singh, M. K., Sahoo, D. K., & Desai, P. P. D. (2023). In silico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CL pro) using deep learning. Frontiers in Microbiology, 14(June), 1194794. https://doi.org/10.3389/fmicb.2023.1194794
  • Prabhavathi, H., Dasegowda, K. R., Renukananda, K. H., Karunakar, P., Lingaraju, K., & Raja Naika, H. (2022). Molecular docking and dynamic simulation to identify potential phytocompound inhibitors for EGFR and HER2 as anti-breast cancer agents. Journal of Biomolecular Structure & Dynamics, 40(10), 4713–4724. https://doi.org/10.1080/07391102.2020.1861982
  • Prasanna, S., & Doerksen, R. J. (2009). Topological polar surface area: A useful descriptor in 2D-QSAR. Current Medicinal Chemistry, 16(1), 21–41. https://doi.org/10.2174/092986709787002817
  • Purba, E. R., Saita, E. I., & Maruyama, I. N. (2017). Activation of the EGF receptor by ligand binding and oncogenic mutations: The ‘rotation model’. Cells, 6(2), 13. https://doi.org/10.3390/cells6020013
  • Puthanveedu, V., & Muraleedharan, K. (2022). Phytochemicals as potential inhibitors for COVID-19 revealed by molecular docking, molecular dynamic simulation and DFT studies. Structural Chemistry, 33(5), 1423–1443. https://doi.org/10.1007/s11224-022-01982-4
  • Pyasi, S., Jonniya, N. A., Sk, M. F., Nayak, D., & Kar, P. (2022). Finding potential inhibitors against RNA-dependent RNA polymerase (RdRp) of bovine ephemeral fever virus (BEFV): An in-silico study. Journal of Biomolecular Structure & Dynamics, 40(20), 10403–10421. https://doi.org/10.1080/07391102.2021.1946714
  • Qidwai, T. (2017). QSAR modeling, docking and ADMET studies for exploration of potential anti-malarial compounds against Plasmodium falciparum. In Silico Pharmacology, 5(1), 1–13. https://doi.org/10.1007/s40203-017-0026-0
  • Rafatian, G., Khodagholi, F., Farimani, M. M., Abraki, S. B., & Gardaneh, M. (2012). Increase of autophagy and attenuation of apoptosis by salvigenin promote survival of SH-SY5Y cells following treatment with H2O2. Molecular and Cellular Biochemistry, 371(1–2), 9–22. https://doi.org/10.1007/s11010-012-1416-6
  • Ramesh, P., Karuppasamy, R., & Veerappapillai, S. (2023). Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer. Medical Oncology, 40(1), 56. https://doi.org/10.1007/s12032-022-01924-4
  • Rashid, F., Javaid, A., Mahmood-Ur-Rahman, Ashfaq, U. A., Sufyan, M., Alshammari, A., Alharbi, M., Nisar, M. A., & Khurshid, M. (2022). Integrating pharmacological and computational approaches for the phytochemical analysis of Syzygium cumini and its anti-diabetic potential. Molecules, 27(17), 5734. https://doi.org/10.3390/molecules27175734
  • Rashid, M., Maqbool, A., Shafiq, N., Bin Jardan, Y. A., Parveen, S., Bourhia, M., Nafidi, H.-A., & Khan, R. A. (2023). The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies. Frontiers in Chemistry, 11, 1197665. https://doi.org/10.3389/fchem.2023.1197665
  • Renju, G. L., Manoharan, S., Balakrishnan, S., & Senthil, N. (2007). Chemopreventive and antilipidperoxidative potential of Clerodendron inerme (L) Gaertn in 7,12-dimethylbenz(a)anthracene induced skin carcinogenesis in Swiss Albino mice. Pakistan Journal of Biological Sciences, 10(9), 1465–1470. https://doi.org/10.3923/pjbs.2007.1465.1470
  • Rosário-Ferreira, N., Baptista, S. J., Barreto, C. A. V., Rodrigues, F. E. P., Silva, T. F. D., Ferreira, S. G. F., Vitorino, J. N. M., Melo, R., Victor, B. L., Machuqueiro, M., & Moreira, I. S. (2021). In silico end-to-end protein-ligand interaction characterization pipeline: The case of SARS-CoV-2. ACS Synthetic Biology, 10(11), 3209–3235. https://doi.org/10.1021/acssynbio.1c00368
  • Sadybekov, A. V., & Katritch, V. (2023). Computational approaches streamlining drug discovery. Nature, 616(7958), 673–685. https://doi.org/10.1038/s41586-023-05905-z
  • Saleh, N. A. (2023). In-silico study: Docking simulation and molecular dynamics of peptidomimetic fullerene-based derivatives against SARS-CoV-2 M pro. 3 Biotech, 13(6), 185. https://doi.org/10.1007/s13205-023-03608-w
  • Sandoval, P. J., Zorn, K. M., Clark, A. M., Ekins, S., & Wright, S. H. (2018). Assessment of substrate-dependent ligand interactions at the organic cation transporter OCT2 using six model substrates. Molecular Pharmacology, 94(3), 1057–1068. https://doi.org/10.1124/mol.117.111443
  • Sarvestani, N. N., Sepehri, H., Delphi, L., & Farimani, M. M. (2018). Eupatorin and Salvigenin potentiate doxorubicin-induced apoptosis and cell cycle arrest in HT-29 and SW948 human colon cancer cells. Asian Pacific Journal of Cancer Prevention, 19(1), 131–139. https://doi.org/10.22034/APJCP.2018.19.1.131
  • Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020). Towards reproducible computational drug discovery. Journal of Cheminformatics, 12(1), 9. https://doi.org/10.1186/s13321-020-0408-x
  • Shahi, M., & Azarakhshi, F. (2023). Theoretical study of interaction between temozolomide anticancer drug and hydroxyethyl carboxymethyl cellulose nanocarriers for targeted drug delivery by DFT quantum mechanical calculation. BMC Chemistry, 17(1), 114. https://doi.org/10.1186/s13065-023-01029-7
  • Shao, H., Chen, J., Li, A., Ma, L., Tang, Y., Chen, H., Chen, Y., & Liu, J. (2023). Salvigenin suppresses hepatocellular carcinoma glycolysis and chemoresistance through inactivating the PI3K/AKT/GSK-3β pathway. Applied Biochemistry and Biotechnology, 195(8), 5217–5237. https://doi.org/10.1007/s12010-023-04511-z
  • Sharma, S., Kumar, V., Yaseen, M., S Abouzied, A., Arshad, A., Bhat, M. A., Naglah, A. M., Patel, C. N., Sivakumar, P. K., Sourirajan, A., Shahzad, A., & Dev, K. (2023). Phytochemical analysis, in vitro biological activities, and computer-aided analysis of Potentilla nepalensis Hook compounds as potent. Molecules, 28(13), 5108. https://doi.org/10.3390/molecules28135108
  • Sherin, D. R., & Manojkumar, T. K. (2021). Exploring the selectivity of guanine scaffold in anticancer drug development by computational repurposing approach. Scientific Reports, 11(1), 16251. https://doi.org/10.1038/s41598-021-95507-4
  • Singh, V. B. (2014). Spectroscopic signatures and structural motifs in isolated and hydrated caffeine: A computational study. RSC Advances, 4(101), 58116–58126. https://doi.org/10.1039/C4RA09749A
  • Singh, V., Patra, S., Murugan, N. A., Toncu, D.-C., & Tiwari, A. (2022). Recent trends in computational tools and data-driven modeling for advanced materials. Materials Advances, 3(10), 4069–4087. https://doi.org/10.1039/D2MA00067A
  • Srisook, K., Srisook, E., Nachaiyo, W., Chan-In, M., Thongbai, J., Wongyoo, K., Chawsuanthong, S., Wannasri, K., Intasuwan, S., & Watcharanawee, K. (2015). Bioassay-guided isolation and mechanistic action of anti-inflammatory agents from Clerodendrum inerme leaves. Journal of Ethnopharmacology, 165, 94–102. https://doi.org/10.1016/j.jep.2015.02.043
  • Stéen, E. J. L., Vugts, D. J., & Windhorst, A. D. (2022). The application of in silico methods for prediction of blood-brain barrier permeability of small molecule PET tracers. Frontiers in Nuclear Medicine, 2(March), 1–14. https://doi.org/10.3389/fnume.2022.853475
  • Sumera, Anwer, F., Waseem, M., Fatima, A., Malik, N., Ali, A., & Zahid, S. (2022). Molecular docking and molecular dynamics studies reveal glioblastoma multiforme. Molecules, 27(21), 7198. https://doi.org/10.3390/molecules27217198
  • Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica. B, 12(7), 3049–3062. https://doi.org/10.1016/j.apsb.2022.02.002
  • Tayeh, M., Hiransai, P., Kommen, H., & Watanapokasin, R. (2020). Anti-migration and anti-invasion abilities of methanolic leaves extract of clerodendrum inerme on lung cancer cells. Pharmacognosy Journal, 12(5), 1024–1031. https://doi.org/10.5530/pj.2020.12.145
  • Todsaporn, D., Mahalapbutr, P., Poo-Arporn, R. P., Choowongkomon, K., & Rungrotmongkol, T. (2022). Structural dynamics and kinase inhibitory activity of three generations of tyrosine kinase inhibitors against wild-type, L858R/T790M, and L858R/T790M/C797S forms of EGFR. Computers in Biology and Medicine, 147(April), 105787. https://doi.org/10.1016/j.compbiomed.2022.105787
  • Tuasha, N., & Petros, B. (2020). Heterogeneity of tumors in breast cancer: Implications and prospects for prognosis and therapeutics. Scientifica, 2020, 4736091. https://doi.org/10.1155/2020/4736091
  • Udeozor, P. A., Ibiam, U. A., Uti, D. E., Umoru, G. U., Onwe, E. N., Mbonu, F. O., Omang, W. A., Ijoganu, S. I., Anaga, C. O., Mbah, J. O., & Nwadum, S. K. (2022). Antioxidant and anti-anemic effects of ethanol leaf extracts of Mucuna poggei and Telfairia occidentalis in phenyl-hydrazine-induced anemia in Wistar Albino rats. Ibnosina Journal of Medicine and Biomedical Sciences, 14(3), 116–126. https://doi.org/10.1055/s-0042-1756684
  • Uno, M., Kokuryo, T., Yokoyama, Y., Senga, T., & Nagino, M. (2016). α-Bisabolol inhibits invasiveness and motility in pancreatic cancer through KISS1R. Anticancer Research, 36(2), 583–590.
  • Van De Waterbeemd, H. (2007). In silico models to predict oral absorption. In J. B. Taylor & D. J. Triggle (Eds.), Comprehensive medicinal chemistry II (2nd ed., pp. 669–697). Elsevier Science. https://doi.org/10.1016/b0-08-045044-x/00145-0
  • van Maaren, M. C., de Munck, L., Strobbe, L. J. A., Sonke, G. S., Westenend, P. J., Smidt, M. L., Poortmans, P. M. P., & Siesling, S. (2019). Ten-year recurrence rates for breast cancer subtypes in the Netherlands: A large population-based study. International Journal of Cancer, 144(2), 263–272. https://doi.org/10.1002/ijc.31914
  • Vijay, U., Gupta, S., Mathur, P., Suravajhala, P., & Bhatnagar, P. (2018). Microbial mutagenicity assay: Ames test. Bio-protocol, 8(6), e2763. https://doi.org/10.21769/bioprotoc.2763
  • Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., & Hou, T. (2019). End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chemical Reviews, 119(16), 9478–9508. https://doi.org/10.1021/acs.chemrev.9b00055
  • Wang, J. H., Luan, F., He, X. D., Wang, Y., & Li, M. X. (2018). Traditional uses and pharmacological properties of Clerodendrum phytochemicals. Journal of Traditional and Complementary Medicine, 8(1), 24–38. https://doi.org/10.1016/j.jtcme.2017.04.001.
  • Wang, N.-N., Huang, C., Dong, J., Yao, Z.-J., Zhu, M.-F., Deng, Z.-K., Lv, B., Lu, A.-P., Chen, A. F., & Cao, D.-S. (2017). Predicting human intestinal absorption with modified random forest approach: A comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Advances, 7(31), 19007–19018. https://doi.org/10.1039/C6RA28442F
  • Wee, P., & Wang, Z. (2017). Epidermal growth factor receptor cell proliferation signaling pathways. Cancers, 9(5), 52. https://doi.org/10.3390/cancers9050052
  • Wisessombat, S., & Tayeh, M. (2021). In vitro wound healing potential and antimicrobial activity of Clerodendrum inerme leave extracts. Pharmacognosy Journal, 13(6), 1542–1548. https://doi.org/10.5530/pj.2021.13.196
  • Wu, S., Peng, L., Sang, H., Li, Q. P., & Cheng, S. (2018). Anticancer effects of α-Bisabolol in human non-small cell lung carcinoma cells are mediated via apoptosis induction, cell cycle arrest, inhibition of cell migration and invasion and upregulation of P13K/AKT signalling pathway. Journal of the Balkan Union of Oncology, 23(5), 1407–1412.
  • Xing, C., Chen, P., & Zhang, L. (2023). Computational insight into stability-enhanced systems of anthocyanin with protein/peptide. Food Chemistry. Molecular Sciences, 6(December 2022), 100168. https://doi.org/10.1016/j.fochms.2023.100168
  • Yankova, R., Dimov, M., Dobreva, K., & Stoyanova, A. (2019). Electronic structure, reactivity, and Hirshfeld surface analysis of carvone. Journal of Chemical Research, 43(9–10), 319–329. https://doi.org/10.1177/1747519819863957
  • Yasmin, F., Nazli, Z.-I.-H., Shafiq, N., Aslam, M., Bin Jardan, Y. A., Nafidi, H.-A., & Bourhia, M. (2023). Plant-based bioactive phthalates derived from Hibiscus rosa-sinensis: As in vitro and in silico enzyme inhibition. ACS Omega, 8(36), 32677–32689. https://doi.org/10.1021/acsomega.3c03342
  • Yousaf, M., Ismail, S., Ullah, A., & Bibi, S. (2022). Immuno-informatics pro fi ling of monkeypox virus cell surface binding protein for designing a next generation multi-valent peptide-based vaccine. Frontiers in Immunology, 13, 1035924. https://doi.org/10.3389/fimmu.2022.1035924
  • Yun, C.-H., Boggon, T. J., Li, Y., Woo, M. S., Greulich, H., Meyerson, M., & Eck, M. J. (2007). Structures of lung cancer-derived EGFR mutants and inhibitor complexes: Mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell, 11(3), 217–227. https://doi.org/10.1016/j.ccr.2006.12.017
  • Zadorozhnii, P. V., Kiselev, V. V., & Kharchenko, A. V. (2022). In silico ADME profiling of salubrinal and its analogues. Future Pharmacology, 2(2), 160–197. https://doi.org/10.1016/j.ejps.2020.105538
  • Zhang, Y., Cai, Y., Zhang, S.-R., Li, C.-Y., Jiang, L.-L., Wei, P., & He, M.-F. (2021). Mechanism of hepatotoxicity of first-line tyrosine kinase inhibitors: Gefitinib and afatinib. Toxicology Letters, 343, 1–10. https://doi.org/10.1016/j.toxlet.2021.02.003
  • Zhao, J., Cao, Y., & Zhang, L. (2020). Exploring the computational methods for protein-ligand binding site prediction. Computational and Structural Biotechnology Journal, 18, 417–426. https://doi.org/10.1016/j.csbj.2020.02.008

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.