Skip to main content
SpringerLink
Log in

Discovery of novel indoleamine 2,3-dioxygenase-1 (IDO-1) inhibitors: pharmacophore-based 3D-QSAR, Gaussian field-based 3D-QSAR, docking, and binding free energy studies

  • Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

Indoleamine 2,3-dioxygenase-1 (IDO-1) is a heme-containing enzyme that initiates the kynurenine pathway by catalyzing the first step in l-tryptophan catabolism. IDO-1 has been shown to play an important role in immunosuppressive mechanisms and tumor evasion, making it an attractive target for therapeutic intervention, a computer-aided drug design (CADD) approach was applied to a set of 34 of 4,5-Disubstituted 1,2,3-triazole derivatives that had been evaluated for their IC50 values against indoleamine 2,3-dioxygenase 1 (IDO1) enzyme. By employing atom-based 3-QSAR pharmacophores and field-based 3D-QSARs, the study identified specific chemical groups and structural locations that could be modified to enhance the compounds’ activity against IDO1 in order to discover more effective inhibitors. The study utilized atom-based 3-QSAR pharmacophores and field-based 3D-QSARs to model the 4,5-Disubstituted 1,2,3-triazole derivatives against IDO1, leading to the identification of specific chemical groups and structural regions that could be altered to enhance the compounds’ activity. The triazole derivatives were subjected to molecular docking, yielding docking scores of − 7.12 kcal/mol for compound 25, and − 7.1, − 7.4, − 7.4, − 8.1, and − 8.3 kcal/mol for the five newly designed molecules T01-T05, respectively. Moreover, an assessment of the free energy was conducted to determine the energetic factors that contribute to the stability of the compounds within the enzyme’s binding site.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Dunn GP, Old LJ, Schreiber RD (2004) The immunobiology of cancer immunosurveillance and immunoediting. Immunity 2:137–148. https://doi.org/10.1016/j.immuni.2004.07.017

    Article  Google Scholar 

  2. Zou W (2005) Immunosuppressive networks in the tumour environment and their therapeutic relevance. Nat Rev Cancer 5:263–274. https://doi.org/10.1038/nrc1586

    Article  PubMed  CAS  Google Scholar 

  3. Hoos A (2016) Development of immuno-oncology drugs—from CTLA4 to PD1 to the next generations. Nat Rev Drug Discov 15:235–247. https://doi.org/10.1038/nrd.2015.35

  4. He X, Xu C (2020) Immune checkpoint signaling and cancer immunotherapy. Cell Res 30:660–669. https://doi.org/10.1038/s41422-020-0343-4

    Article  PubMed  PubMed Central  Google Scholar 

  5. Schoenfeld AJ, Hellmann MD (2020) Acquired resistance to immune checkpoint inhibitors. Cancer Cell 37:443–455. https://doi.org/10.1038/s41422-020-0343-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. van Baren N, Van den Eynde BJ (2015) Tumoral immune resistance mediated by enzymes that degrade tryptophan. Cancer Immunol Res 3:978–985. https://doi.org/10.1158/2326-6066.CIR-15-0095

    Article  PubMed  CAS  Google Scholar 

  7. Platten M, Wick W, Van den Eynde BJ (2012) Tryptophan catabolism in cancer: beyond IDO and tryptophan depletion tryptophan catabolism in cancer. Cancer Res 72:5435–5440. https://doi.org/10.1158/0008-5472.CAN-12-0569

    Article  PubMed  CAS  Google Scholar 

  8. Munn DH, Mellor AL (2013) Indoleamine 2, 3 Dioxygenase and metabolic control of immune responses. Trends Immunol 34:137–143. https://doi.org/10.1016/j.it.2012.10.001

    Article  PubMed  CAS  Google Scholar 

  9. Xiao Y, Freeman GJ (2015) The microsatellite instable subset of colorectal cancer is a particularly good candidate for checkpoint blockade immunotherapy. Cancer Discov 5:16–18. https://doi.org/10.1016/j.it.2012.10.001

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Phan T, Nguyen VH, D’Alincourt MS, Manuel ER, Kaltcheva T, Tsai W, Blazar BR, Diamond DJ, Melstrom LG (2020) Salmonella-mediated therapy targeting Indoleamine 2, 3-Dioxygenase 1 (IDO) activates innate immunity and mitigates colorectal cancer growth. Cancer Gene Ther 27:235–245. https://doi.org/10.1038/s41417-019-0089-7

    Article  PubMed  CAS  Google Scholar 

  11. Alexandre JAC, Swan MK, Latchem MJ, Boyall D, Pollard JR, Hughes SW, Westcott J (2018) New 4‐amino‐1, 2, 3‐triazole inhibitors of Indoleamine 2, 3‐Dioxygenase form a long‐lived complex with the enzyme and display exquisite cellular potency. ChemBioChem 19:552–561. https://doi.org/10.1002/cbic.201700560

  12. Siu L, Gelmon K, Chu Q, Pachynski R, Alese O, Basciano P, Walker J, Mitra P, Zhu L, Phillips P (2017) BMS-986205, an optimized Indoleamine 2, 3-Dioxygenase 1 (IDO1) inhibitor, is well tolerated with potent pharmacodynamic (PD) activity, alone and in combination with nivolumab (nivo) in advanced cancers in a phase 1/2a trial. In Cancer Res 77

  13. Yue EW, Sparks R, Polam P, Modi D, Douty B, Wayland B, Glass B, Takvorian A, Glenn J, Zhu W (2017) INCB24360 (epacadostat), a highly potent and selective Indoleamine-2, 3-Dioxygenase 1 (IDO1) inhibitor for immuno-oncology. ACS Med Chem Lett 8:486–491. https://doi.org/10.1021/acsmedchemlett.6b00391

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Kumar S, Waldo JP, Jaipuri FA, Marcinowicz A, Van Allen C, Adams J, Kesharwani T, Zhang X, Metz R, Oh AJ (2019) Discovery of clinical candidate (1 R, 4 r)-4-((R)-2-((S)-6-Fluoro-5 H-Imidazo [5, 1-a] Isoindol-5-Yl)-1-Hydroxyethyl) Cyclohexan-1-Ol (Navoximod), a potent and selective inhibitor of Indoleamine 2, 3-Dioxygenase 1. J Med Chem 62:6705–6733. https://doi.org/10.1021/acs.jmedchem.9b00662

    Article  PubMed  CAS  Google Scholar 

  15. Crosignani S, Bingham P, Bottemanne P, Cannelle H, Cauwenberghs S, Cordonnier M, Dalvie D, Deroose F, Feng JL, Gomes B (2017) Discovery of a novel and selective Indoleamine 2, 3-Dioxygenase (IDO-1) inhibitor 3-(5-Fluoro-1 H-Indol-3-Yl) Pyrrolidine-2, 5-Dione (EOS200271/PF-06840003) and its characterization as a potential clinical candidate. J Med Chem 60:9617–9629. https://doi.org/10.1021/acs.jmedchem.7b00974

    Article  PubMed  CAS  Google Scholar 

  16. Muller AJ, Manfredi MG, Zakharia Y, Prendergast GC (2019) Inhibiting IDO pathways to treat cancer: lessons from the ECHO-301 trial and beyond. Semin Immunopathol Springer 41:41–48. https://doi.org/10.1007/s00281-018-0702-0

  17. El Mchichi L, Tabti K, Kasmi R, El-Mernissi R, El Aissouq A, En-nahli F, Belhassan A, Lakhlifi T, Bouachrine M (2022) 3D-QSAR study, docking molecular and simulation dynamic on series of benzimidazole derivatives as anti-cancer agents. J Indian Chem Soc 99:100582. https://doi.org/10.1016/j.jics.2022.100582

  18. Tabti K (2020) QSAR studies of new compounds based on thiazole derivatives as pin1 inhibitors via statistical methods. RHAZES: Green and Applied Chemistry 9:70–91. https://doi.org/10.48419/IMIST.PRSM/rhazes-v9.21394

  19. El Masaoudy Y, Tabti K, Koubi Y, Maghat H, Lakhlifi T, Bouachrine M (2023) In Silico Design of new Pyrimidine-2, 4-Dione derivatives as promising inhibitors for HIV reverse transcriptase-associated RNase H using 2D-QSAR modeling and (ADME/Tox) properties. Moroc J Chem 11:300–317. https://doi.org/10.48317/IMIST.PRSM/morjchem-v11i2.35455

  20. Tabti K, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2020) 2D and 3D-QSAR/CoMSIA Comparative study on a series of thiazole derivatives as SDHI Inhibitors. Maghreb J Pure & Appl Sci 6:73–90. https://doi.org/10.48383/IMIST.PRSM/mjpas-v6i2.23108

  21. Kranthi RK, Manohar S, Talluri VR, Rawat DS (2015) Insights into activity enhancement of 4-aminoquinoline-based hybrids using atom-based and field-based QSAR studies. Med Chem Res 24:1136–1154. https://doi.org/10.1007/s00044-014-1195-6

    Article  CAS  Google Scholar 

  22. Al-Sha’er MA, Taha M, Alelaimat MA (2023) Development of Phosphoinositide 3-Kinase Delta (PI3Kδ) inhibitors as potential anticancer agents through the generation of ligand-based pharmacophores and biological screening. Med Chem Res 17:30. https://doi.org/10.1007/s00044-023-03057-3

  23. Zhang L, Lai F, Chen X, Xiao Z (2020) Identification of potential Indoleamine 2, 3-Dioxygenase 1 (IDO1) inhibitors by an FBG-based 3D QSAR pharmacophore model. J Mol Graph Model 99:107628. https://doi.org/10.1016/j.jmgm.2020.107628

  24. Jain S, Bhardwaj B, Amin SA, Adhikari N, Jha T, Gayen S (2020) Exploration of good and bad structural fingerprints for inhibition of Indoleamine-2, 3-Dioxygenase enzyme in cancer Immunotherapy Using Monte Carlo Optimization and Bayesian Classification QSAR Modeling. J Biomol Struct Dyn 38:1683–1696. https://doi.org/10.1080/07391102.2019.1615000

    Article  PubMed  CAS  Google Scholar 

  25. Tabti K, Elmchichi L, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2022) Molecular modelling of antiproliferative inhibitors based on SMILES descriptors using Monte-Carlo method, docking, MD simulations and ADME/Tox studies. Mol Simul 48:1575–1591. https://doi.org/10.1080/08927022.2022.2110246

    Article  CAS  Google Scholar 

  26. Hajji H, Tabti K, En-nahli F, Bouamrane S, Lakhlifi T, Ajana MA, Bouachrine M (2021) In silico investigation on the beneficial effects of medicinal plants on diabetes and obesity: molecular docking, molecular dynamic simulations, and ADMET studies. Biointerface Res Appl Chem 11:6933–6949. https://doi.org/10.33263/BRIAC115.69336949

  27. Tabti K, Abdessadak O, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2023) Design and development of novel spiro-oxindoles as potent antiproliferative agents using quantitative structure activity based Monte Carlo method, docking molecular, molecular dynamics, free energy calculations, and pharmacokinetics/toxicity studies. J Mol Struct 1284:135404. https://doi.org/10.1016/j.molstruc.2023.135404

  28. Tabti K, Ahmad I, Zafar I, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2023) Profiling the structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches. Comput Biol Chem 107855. https://doi.org/10.1016/j.compbiolchem.2023.107855

  29. Panda S, Pradhan N, Chatterjee S, Morla S, Saha A, Roy A, Kumar S, Bhattacharyya A, Manna D (2019) 4, 5-Disubstituted 1, 2, 3-Triazoles: effective inhibition of Indoleamine 2, 3-Dioxygenase 1 enzyme regulates T cell activity and mitigates tumor growth. Sci Rep 9:18455. https://doi.org/10.1038/s41598-019-54963-9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Tabti K, Elmchichi L, Sbai A, Maghat H, Bouachrine M, Lakhlifi T, Ghosh A (2022) In silico design of novel PIN1 inhibitors by combined of 3D-QSAR, molecular docking, molecular dynamic simulation and ADMET studies. J Mol Struct 1253:132291. https://doi.org/10.1016/j.molstruc.2021.132291

  31. Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6:1509–1519. https://doi.org/10.1021/ct900587b

    Article  PubMed  CAS  Google Scholar 

  32. Kalva S, Vinod D, Saleena LM (2013) Field-and Gaussian-based 3D-QSAR studies on barbiturate analogs as MMP-9 inhibitors. Med Chem Res 22:5303–5313. https://doi.org/10.1007/s00044-013-0479-6

    Article  CAS  Google Scholar 

  33. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for PK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21:681–691. https://doi.org/10.1007/s10822-007-9133-z

    Article  PubMed  CAS  Google Scholar 

  34. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. https://doi.org/10.1007/s10822-006-9087-6

  35. Mohamadi F, Richards NG, Guida WC, Liskamp R, Lipton M, Caufield C, Chang G, Hendrickson T, Still WC (1990) Macromodel—an integrated software system for modeling organic and bioorganic molecules using molecular mechanics. J Comput Chem 11:440–467. https://doi.org/10.1002/jcc.540110405

    Article  CAS  Google Scholar 

  36. Dietrich M, Heinze J, Krieger C, Neugebauer FA (1996) Electrochemical oxidation and structural changes of 5, 6-Dihydrobenzo [c] Cinnolines. J Am Chem Soc 118:5020–5030. https://doi.org/10.1021/ja952722+

    Article  CAS  Google Scholar 

  37. Sindhu T, Srinivasan P (2014) Pharmacophore modeling, 3D-QSAR and molecular docking studies of benzimidazole derivatives as potential FXR agonists. J Recept Signal Transduct Res 34:241–253. https://doi.org/10.3109/10799893.2014.885048

    Article  PubMed  CAS  Google Scholar 

  38. Tabti K, Elmchichi L, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2022) HQSAR, CoMFA, CoMSIA docking studies and simulation MD on quinazolines/quinolines derivatives for DENV virus inhibitory activity. Chem Afr 5:1937–1958. https://doi.org/10.1007/s42250-022-00484-4

    Article  CAS  Google Scholar 

  39. Teli MK (2012) Pharmacophore generation and atom-based 3D-QSAR of N-Iso-Propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors. Bioorganic Med Chem Lett 2:1–10. https://doi.org/10.1186/2191-2858-2-25

    Article  CAS  Google Scholar 

  40. Yao K, Liu P, Liu H, Wei Q, Yang J, Cao P, Lai Y (2019) 3D-QSAR, molecular docking and molecular dynamics simulations study of 3-Pyrimidin-4-Yl-Oxazolidin-2-One derivatives to explore the structure requirements of mutant IDH1 inhibitors. J Mol Struct 1189:187–202. https://doi.org/10.1016/j.molstruc.2019.03.092

    Article  CAS  Google Scholar 

  41. Palafox MA, Kattan D, de Pedraza Velasco ML, Isasi J, Posada-Moreno P, Rani K, Singh SP, Rastogi VK (2022) Base pairs with 4-Amino-3-Nitrobenzonitrile: comparison with the natural WC pairs. Dimer and tetramer forms, infrared and Raman spectra, and several proposed antiviral modified nucleosides. J Biomol Struct Dyn 1–23. https://doi.org/10.1080/07391102.2022.2069864

  42. Tojo S, Kohno T, Tanaka T, Kamioka S, Ota Y, Ishii T, Kamimoto K, Asano S, Isobe Y (2014) Crystal structures and structure–activity relationships of imidazothiazole derivatives as IDO1 inhibitors. ACS Med Chem Lett 5:1119–1123. https://doi.org/10.1021/ml500247w

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank (Www. Rcsb. Org). Nucleic Acids Res 28:235–242

  44. Madhavi SG, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234. https://doi.org/10.1007/s10822-013-9644-8

    Article  CAS  Google Scholar 

  45. Tabti K, Baammi S, ElMchichi L, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2022) Computational investigation of pyrrolidin derivatives as novel GPX4/MDM2–P53 inhibitors using 2D/3D-QSAR, ADME/Toxicity, molecular docking, molecular dynamics simulations, and MM-GBSA free energy. Struct Chem 33:1019–1039. https://doi.org/10.1007/s11224-022-01903-5

    Article  CAS  Google Scholar 

  46. Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, Shaw DE, Friesner RA (2004) A hierarchical approach to all‐atom protein loop prediction. Proteins: Struct Funct Bioinfo 55:351–367. https://doi.org/10.1002/prot.10613

  47. Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL (2016) OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12:281–296. https://doi.org/10.1021/acs.jctc.5b00864

    Article  PubMed  CAS  Google Scholar 

  48. Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA (2011) The VSGB 2.0 Model: a next generation energy model for high resolution protein structure modeling. Proteins: Struct. Funct. Bioinfo. 79:2794–2812. https://doi.org/10.1002/prot.23106

  49. Rajagopal K, Varakumar P, Baliwada A, Byran G (2020) Activity of phytochemical constituents of Curcuma Longa (turmeric) and Andrographis Paniculata against coronavirus (COVID-19): an in silico approach. Future J Pharm Sci 6:1–10. https://doi.org/10.1186/s43094-020-00126-x

    Article  Google Scholar 

  50. Ioakimidis L, Thoukydidis L, Mirza A, Naeem S, Reynisson J (2008) Benchmarking the reliability of QikProp. correlation between experimental and predicted values. QSAR Comb Sci 27:445–456. https://doi.org/10.1002/qsar.200730051

  51. Onguéné PA, Ntie-Kang F, Mbah JA, Lifongo LL, Ndom JC, Sippl W, Mbaze LM (2014) The potential of anti-malarial compounds derived from African medicinal plants, part III: an in silico evaluation of drug metabolism and pharmacokinetics profiling. organic Med Chem Lett 4:1–9. https://doi.org/10.1186/s13588-014-0006-x

  52. Kirchmair J, Markt P, Distinto S, Wolber G, Langer T (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? J Comput Aided Mol Des 22:213–228. https://doi.org/10.1007/s10822-007-9163-6

    Article  PubMed  CAS  Google Scholar 

  53. Dessalew G, Beyene A, Nebiyu A, Astatkie T (2018) Effect of brewery spent diatomite sludge on trace metal availability in soil and uptake by wheat crop, and trace metal risk on human health through the consumption of wheat grain. Heliyon 4:e00783. https://doi.org/10.1016/j.heliyon.2018.e00783

  54. Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “Early Recognition” problem. J Chem Inf Model 47:488–508. https://doi.org/10.1021/ci600426e

    Article  PubMed  CAS  Google Scholar 

  55. Halgren TA (2009) Identifying applications, and recent advances of protein-ligand docking in structure-based drug design. J Chem Inf Model 49:377–389

    Article  PubMed  CAS  Google Scholar 

  56. Tabti K, Baammi S, Sbai A, Maghat H, Lakhlifi T, Bouachrine M (2023) Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques. J Biomol Struct 1–17. https://doi.org/10.1080/07391102.2023.2183032

  57. Tabti K, Hajji H, Sbai A, Maghat H, Bouachrine M, Lakhlifi T (2023) Identification of a potential thiazole inhibitor against biofilms by 3D QSAR, molecular docking, DFT analysis, MM-PBSA binding energy calculations, and molecular dynamics simulation. Phys Chem Res 11:369–389. https://doi.org/10.22036/PCR.2022.335657.2068

Download references

Acknowledgements

We are grateful to the “Association Marocaine des Chimistes Théoriciens” (AMCT) for its pertinent help concerning the programs.

Author information

Authors and Affiliations

  1. Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco

    Kamal Tabti, Abdelouahid Sbai, Hamid Maghat, Tahar Lakhlifi & Mohammed Bouachrine

  2. High School of Technology Khenifra, Sultan Moulay Sliman University, Beni Mellal, Morocco

    Mohammed Bouachrine

Authors
  1. Kamal Tabti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelouahid Sbai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Maghat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tahar Lakhlifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammed Bouachrine
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Kamal Tabti: Data curation, Writing—original draft. Abdelouahid Sbai: Conceptualization, Methodology, Software. Hamid Maghat: Visualization, Investigation, Supervision. Tahar Lakhlifi: Writing—review and editing.Mohammed Bouachrin: Software, Validation. All authors commented on previous versions of the manuscript and approved the final manuscript.

Corresponding author

Correspondence to Abdelouahid Sbai.

Ethics declarations

Ethics approval

This chapter does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tabti, K., Sbai, A., Maghat, H. et al. Discovery of novel indoleamine 2,3-dioxygenase-1 (IDO-1) inhibitors: pharmacophore-based 3D-QSAR, Gaussian field-based 3D-QSAR, docking, and binding free energy studies. Struct Chem 35, 135–160 (2024). https://doi.org/10.1007/s11224-023-02213-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-023-02213-0

Keywords

Search

Navigation