Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation
Abstract
:1. Introduction
2. Results and Discussions
2.1. JAK2 Active and Decoy Datasets and Its Preprocessing Using RDKit
2.2. Deep-Learning Model Setup, Training, and Evaluation
2.3. Prediction of JAK2 Inhibitory Potential from FDA-Approved Drugs
2.4. Structural Analysis of the JAK2 Protein
2.5. The Binding Pocket Analysis
2.6. Molecular Docking Analysis
2.7. Binding Interaction Analysis against JAK2
2.8. Experimental Validation
2.9. Structural Evaluation and Similarity Comparison
3. Methodology
3.1. JAK2 Datasets and FDA-Approved Drug Library
3.2. Molecular Descriptor Generation Using RDKit
3.3. Deep Learning Architecture
3.4. JAK2 Structure Retrieval
3.5. Prediction of Active Binding Site
3.6. Molecular Docking
3.7. Binding Interaction Analysis
3.8. JAK2 Kinase Inhibitory Activity Assay
3.9. Statistical Analysis
4. Conclusions
5. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Precision | Recall | F1 score | Accuracy | Specificity | |
---|---|---|---|---|---|
Training | 1.000 | 0.979 | 0.989 | 0.999 | 1.000 |
Validation | 0.833 | 0.625 | 0.714 | 0.994 | 0.999 |
Test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Smiles | Neg | Pos | Name | Target | Use |
---|---|---|---|---|---|
N#CC[C@H](C1CCCC1)n1ccc2ncnc3[nH]ccc23)cn1 | 0.0002 | 0.9998 | Ruxolitinib | JAK Inhibitor | Myelofibrosis, Anti cancer drug |
COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1.Cl | 0.0004 | 0.9996 | Gefitinib | Tyrosine Kinase, EGFR inhibitor | Non-small cell lung carcinoma |
Nc1ncnc2[nH]cnc12 | 0.0007 | 0.9993 | Adenine | Nucleobase | Nucleotide |
c1ncc2nc[nH]c2n1 | 0.0007 | 0.9993 | Purine | Heterocyclic aromatic organic compound | DNA and RNA formation |
CC[C@H](Nc1ncnc2[nH]cnc12)c1nc2cccc(F)c2c(=O)n1-c1ccccc1 | 0.0011 | 0.9989 | Idelalisib | Phosphoinositide 3-kinase inhibitor | Blood cancer |
C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCOCCOCCO4)c1 | 0.0021 | 0.9979 | Icotinib | Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) | Non-small cell lung cancer |
c1coc(CNc2ncnc3nc[nH]c23)c1 | 0.0031 | 0.9969 | Kinetin | Proapoptotic anti-proliferative plant growth regulator | Cell division |
Cl.O=C(O)c1cn(-c2ccc(F)cc2)c2cc(N3CCNCC3)c(F)cc2c1=O | 0.0031 | 0.9969 | Sarafloxcin | Quinolone antibiotic drug | Antibiotic |
CCS(=O)(=O)N1CC(CC#N)(n2cc(-c3ncnc4[nH]ccc34)cn2)C1 | 0.0033 | 0.9967 | Baricitinib | JAK2 inhibitor | Rheumatoid arthritis |
C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12 | 0.0033 | 0.9967 | Tofacitinib | JAKs inhibitor | Rheumatoid arthritis |
Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O.O | 0.0039 | 0.9961 | Vidarabine | Human herpesvirus 1 DNA polymerase | Antiviral |
Nc1ncnc2c1ncn2[C@H]1C[C@H](O)[C@@H](CO)O1.O | 0.0039 | 0.9961 | 2′-Deoxyadenosine | Phosphodiesterase inhibitor | Energy source |
CN(C)C(=O)c1cc2cnc(Nc3ccc(N4CCNCC4)cn3)nc2n1C1CCCC1 | 0.0045 | 0.9955 | Ribociclib | CDK4/CDK6 kinase inhibitor | Metastatic breast cancer |
CCN(CC)CCCC(C)Nc1ccnc2cc(Cl)ccc12 | 0.0051 | 0.9949 | Chloroquine | Heme polymerase inhibitor | Malaria, Rheumatoid arthritis |
COc1cc2nc(N3CCN(C(=O)C4CCCO4)CC3)nc(N)c2cc1OC.Cl.O.O | 0.0074 | 0.9926 | Terazosin | Alpha 1-adrenergic receptor inhibitor | Adrenaline blocker |
CCN(CC)Cc1cc(Nc2ccnc3cc(Cl)ccc23)ccc1O.Cl.Cl.O.O | 0.0077 | 0.9923 | Amodiaquine | Heme polymerase inhibitor | Malaria |
C[C@H](Nc1ncnc2[nH]cnc12)c1cc2cccc(Cl)c2c(=O)n1-c1ccccc1 | 0.0081 | 0.9919 | Duvelisib | PI3K inhibitor | Chronic lymphocytic leukemia |
CC[C@@H]1CN(C(=O)NCC(F)(F)F)C[C@@H]1c1cnc2cnc3[nH]ccc3n12 | 0.0082 | 0.9918 | Upadacitinib | JAK inhibitor | Rheumatoid arthritis |
c1cnc2c(c1)ccc1cccnc12 | 0.0086 | 0.9914 | 1,10-Phenanthroline | Fe(II) chelator | Metal chelator |
C[C@H](Cn1cnc2c(N)ncnc21)OCP(=O)(O)O.O | 0.0100 | 0.9900 | Tenofovir | Nucleotide reverse transcriptase inhibitor | HIV |
Cc1cc(/C=C/C#N)cc(C)c1Nc1ccnc(Nc2ccc(C#N)cc2)n1 | 0.0117 | 0.9883 | Rilpivirine | Transcriptase inhibitor | HIV |
C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1 | 0.0122 | 0.9878 | Erlotinib | Tyrosine kinase, EGFR inhibitor | Non-small cell lung cancer (NSCLC), pancreatic cancer |
Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1 | 0.0172 | 0.9828 | Imatinib | Tyrosine kinase, Bcr-abl inhibitor | Chronic myeloid leukemia |
N#Cc1cc(-c2n[nH]c(-c3ccncc3)n2)ccn1 | 0.0177 | 0.9823 | Topiroxostat | Xanthine oxidase inhibitor | Hyperuricemia (gout) |
O=c1[nH]cnc2[nH]ncc12.[Na+] | 0.0196 | 0.9804 | Allopurinol | Xanthine oxidase inhibitor | Hyperuricemia (gout) |
O.S=c1nc[nH]c2nc[nH]c12 | 0.0298 | 0.9702 | 6-Mercaptopurine | Purine nucleotide synthesis inhibitor | Antimetabolite, Antineoplastic |
C=C[C@H]1CN2CC[C@H]1C[C@H]2[C@H](O)c1ccnc2ccc(OC)cc12.Cl.O.O | 0.0301 | 0.9699 | Quinine | Potassium channel blocker | Antimalarial, Analgesic |
Cl.Cl.c1cnc2cc3c(cc2n1)C1CNCC3C1 | 0.0337 | 0.9663 | Varenicline | Nicotinic receptor blocker | Smoking cessation |
O=c1[nH]cnc2nc[nH]c12 | 0.0365 | 0.9635 | Hypoxanthine | Nucleic acid synthesis | Malaria parasite cultures |
CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4 | 0.0414 | 0.9586 | Staurosporine | PKC inhibitor | Cancer |
Compounds | Cdocker Interaction Energy (kcal/mol) | CDocker Energy (kcal/mol) |
---|---|---|
Ribociclib | −58.0 | −5.3 |
Imatinib | −52.6 | −24.3 |
Staurosporine | −52.2 | 100.1 |
Gefitinib | −50.6 | −15.5 |
Adiphenine | −47.5 | −32.4 |
Difloxacin | −47.2 | −26.1 |
Amodiaquine | −44.4 | −19.8 |
Naratriptan | −42.5 | −31.8 |
Y-33075 | −42.0 | −31.1 |
Rilpivirine | −41.3 | −31.8 |
Tofacitinib | −40.0 | −29.3 |
Dibucaine | −39.8 | −17.6 |
Amsacrine | −39.6 | −8.2 |
Chloroprocaine | −33.4 | −21.5 |
Topiroxostat | −28.8 | −22.6 |
Pinacidil | −28.6 | −20.6 |
Varenicline | −25.6 | 19.9 |
Phenanthroline | −23.0 | −4.8 |
Pargyline | −22.5 | −18.9 |
Allopurinol | −18.6 | −4.0 |
Similarity | Tofacitinib | Ribociclib | Topiroxostat | Amodiaquine | Gefitinib |
---|---|---|---|---|---|
Tofacitinib | - | 0.196970 | 0.130841 | 0.114754 | 0.156716 |
Ribociclib | 0.196970 | - | 0.146154 | 0.146853 | 0.180645 |
Topiroxostat | 0.130841 | 0.146154 | - | 0.186916 | 0.131783 |
Amodiaquine | 0.114754 | 0.146853 | 0.186916 | - | 0.257812 |
Gefitinib | 0.156716 | 0.180645 | 0.131783 | 0.257812 | - |
Name | LogP | Solubility | GI Absorption | BBB Permeation | CYP2D6 Inhibition | Lipinski Violation |
---|---|---|---|---|---|---|
Tofacitinib | 1.22 | −3.34 | High | No | No | 0 |
Ribociclib | 2.12 | −5.51 | High | No | Yes | 0 |
Topiroxostat | 1.38 | −5.24 | High | No | Yes | 0 |
Amodiaquine | 4.6 | −8.18 | High | Yes | Yes | 0 |
Gefitinib | 3.92 | −7.94 | High | Yes | Yes | 0 |
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Yasir, M.; Park, J.; Han, E.-T.; Park, W.S.; Han, J.-H.; Chun, W. Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation. Molecules 2024, 29, 1363. https://doi.org/10.3390/molecules29061363
Yasir M, Park J, Han E-T, Park WS, Han J-H, Chun W. Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation. Molecules. 2024; 29(6):1363. https://doi.org/10.3390/molecules29061363
Chicago/Turabian StyleYasir, Muhammad, Jinyoung Park, Eun-Taek Han, Won Sun Park, Jin-Hee Han, and Wanjoo Chun. 2024. "Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation" Molecules 29, no. 6: 1363. https://doi.org/10.3390/molecules29061363
APA StyleYasir, M., Park, J., Han, E. -T., Park, W. S., Han, J. -H., & Chun, W. (2024). Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation. Molecules, 29(6), 1363. https://doi.org/10.3390/molecules29061363