Centrality Measures in QSPR Modelling of Antiviral Compounds for COVID-19
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The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), causing COVID-19, lacks specific antiviral treatments, escalating the global health crisis. This study employs Quantitative Structure-Property Relationship (QSPR) modelling to explore eight physicochemical properties of antiviral compounds, including Arbidol, Chloroquine, Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide, and Theaflavin. Centrality measures, used as molecular descriptors, quantify the relationship between molecular structure and physicochemical attributes in QSPR studies.
To address missing data for Remdesivir’s Boiling Point (BP), Enthalpy of Vaporisation (E), and Flash Point (FP), correlation-based linear regression imputation was applied using descriptors like Normalised Harmonic Centrality Weight ( 0.976 for BP, 0.973 for E) and Eccentricity Weight ( 0.957 for FP ensuring dataset integrity. Nine graph-based centrality measures were evaluated for their correlation with the physicochemical properties of these drugs. Pearson correlation analysis revealed strong positive correlations, notably Normalised Harmonic Centrality Weight with BP (0.978), E (0.974), and Polar Surface Area (PSA) (0.894), and Eccentricity Weight with Flash Point (0.959), Molar Refractivity (MR) (0.975), and Molar Volume (MV) (0.923). Conversely, Total Closeness Centrality Weight and Leverage Centrality Weight showed significant negative correlations (below 0.5). Single-predictor linear regression models were developed, with robustness assessed via predictive using leave-one-out cross-validation and the PRESS statistic. These models offer interpretable predictions of structural influences on physicochemical behaviour, aiding pharmaceutical researchers in predicting antiviral drug properties for COVID-19 before experimental validation.
The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), causing COVID-19, lacks specific antiviral treatments, escalating the global health crisis. This study employs Quantitative Structure-Property Relationship (QSPR) modelling to explore eight physicochemical properties of antiviral compounds, including Arbidol, Chloroquine, Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide, and Theaflavin. Centrality measures, used as molecular descriptors, quantify the relationship between molecular structure and physicochemical attributes in QSPR studies.
To address missing data for Remdesivir’s Boiling Point (BP), Enthalpy of Vaporisation (E), and Flash Point (FP), correlation-based linear regression imputation was applied using descriptors like Normalised Harmonic Centrality Weight ( 0.976 for BP, 0.973 for E) and Eccentricity Weight ( 0.957 for FP ensuring dataset integrity. Nine graph-based centrality measures were evaluated for their correlation with the physicochemical properties of these drugs. Pearson correlation analysis revealed strong positive correlations, notably Normalised Harmonic Centrality Weight with BP (0.978), E (0.974), and Polar Surface Area (PSA) (0.894), and Eccentricity Weight with Flash Point (0.959), Molar Refractivity (MR) (0.975), and Molar Volume (MV) (0.923). Conversely, Total Closeness Centrality Weight and Leverage Centrality Weight showed significant negative correlations (below 0.5). Single-predictor linear regression models were developed, with robustness assessed via predictive using leave-one-out cross-validation and the PRESS statistic. These models offer interpretable predictions of structural influences on physicochemical behaviour, aiding pharmaceutical researchers in predicting antiviral drug properties for COVID-19 before experimental validation.
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Zhu, N., D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao, B. Huang, W. Shi and R. Lu, A novel coronavirus from patients with pneumonia in China, 2019. New England journal of medicine. 382 (2020), no. 8, 727-733, DOI 10.1056/NEJMoa2001017
Organization, W. H. WHO Director-General's opening remarks at the media briefing on COVID-19. 2020.
Sanders, J. M., M. L. Monogue, T. Z. Jodlowski and J. B. Cutrell, Pharmacologic treatments for coronavirus disease 2019 (COVID-19): a review. Jama. 323 (2020), no. 18, 1824-1836, DOI 10.1001/jama.2020.6019
Wang, Y., D. Zhang, G. Du, R. Du, J. Zhao, Y. Jin, S. Fu, L. Gao, Z. Cheng and Q. Lu, Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. The lancet. 395 (2020), no. 10236, 1569-1578, DOI 10.1016/S0140-6736(20)31022-9
Yao, X., F. Ye, M. Zhang, C. Cui, B. Huang, P. Niu, X. Liu, L. Zhao, E. Dong and C. Song, In vitro antiviral activity and projection of optimized dosing design of hydroxychloroquine for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical infectious diseases. 71 (2020), no. 15, 732-739, DOI 10.1093/cid/ciaa237
Blaising, J., S. J. Polyak and E.-I. Pécheur, Arbidol as a broad-spectrum antiviral: an update. Antiviral research. 107 (2014), no. 84-94, DOI 10.1016/j.antiviral.2014.04.006
Savarino, A., J. R. Boelaert, A. Cassone, G. Majori and R. Cauda, Effects of chloroquine on viral infections: an old drug against today's diseases. The Lancet infectious diseases. 3 (2003), no. 11, 722-727, DOI 10.1016/S1473-3099(03)00806-5
Gagliardini, R., A. Cozzi-Lepri, A. Mariano, F. Taglietti, A. Vergori, A. Abdeddaim, F. Di Gennaro, V. Mazzotta, A. Amendola and G. D’Offizi, No efficacy of the combination of lopinavir/ritonavir plus hydroxychloroquine versus standard of care in patients hospitalized with COVID-19: a non-randomized comparison. Frontiers in Pharmacology. 12 (2021), no. 621676, DOI 10.3389/fphar.2021.621676
Cao, B., Y. Wang, D. Wen, W. Liu, J. Wang, G. Fan, L. Ruan, B. Song, Y. Cai and M. Wei, A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19. New England journal of medicine. 382 (2020), no. 19, 1787-1799, DOI 10.1056/NEJMoa2001282
Franks, M. E., G. R. Macpherson and W. D. Figg, Thalidomide. The Lancet. 363 (2004), no. 9423, 1802-1811, DOI 10.1016/S0140-6736(04)16308-3
Zu, M., F. Yang, W. Zhou, A. Liu, G. Du and L. Zheng, In vitro anti-influenza virus and anti-inflammatory activities of theaflavin derivatives. Antiviral research. 94 (2012), no. 3, 217-224, DOI 10.1016/j.antiviral.2012.04.001
Beigel, J. H., K. M. Tomashek, L. E. Dodd, A. K. Mehta, B. S. Zingman, A. C. Kalil, E. Hohmann, H. Y. Chu, A. Luetkemeyer and S. Kline, Remdesivir for the treatment of Covid-19—preliminary report. New England Journal of Medicine. 383 (2020), no. 19, 1813-1836, DOI 10.1056/NEJMoa2007764
De Clercq, E., Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV. International journal of antimicrobial agents. 33 (2009), no. 4, 307-320, DOI 10.1016/j.ijantimicag.2008.10.010
Sheahan, T. P., A. C. Sims, R. L. Graham, V. D. Menachery, L. E. Gralinski, J. B. Case, S. R. Leist, K. Pyrc, J. Y. Feng and I. Trantcheva, Broad-spectrum antiviral GS-5734 inhibits both epidemic and zoonotic coronaviruses. Science translational medicine. 9 (2017), no. 396, eaal3653, DOI 10.1126/scitranslmed.aal3653
Hung, I. F.-N., K.-C. Lung, E. Y.-K. Tso, R. Liu, T. W.-H. Chung, M.-Y. Chu, Y.-Y. Ng, J. Lo, J. Chan and A. R. Tam, Triple combination of interferon beta-1b, lopinavir–ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomised, phase 2 trial. The lancet. 395 (2020), no. 10238, 1695-1704, DOI 10.1016/S0140-6736(20)31042-4
Todeschini, R. and V. Consonni, Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references; John Wiley & Sons, 2009.
Ivanciuc, O., S. L. Taraviras and D. Cabrol-Bass, Quasi-orthogonal basis sets of molecular graph descriptors as a chemical diversity measure. Journal of Chemical Information and Computer Sciences. 40 (2000), no. 1, 126-134, DOI 10.1021/ci990064x
Todeschini, R. and V. Consonni, Handbook of molecular descriptors; John Wiley & Sons, 2008. DOI: 10.1002/9783527613106.
Ivanciuc, O., Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships §. Current computer-aided drug design. 9 (2013), no. 2, 153-163, DOI 10.2174/1573409911309020002
Kara, Y., Y. S. Özkan, A. Ullah, Y. S. Hamed and M. B. Belay, QSPR modeling of some COVID-19 drugs using neighborhood eccentricity-based topological indices: A comparative analysis. PLoS One. 20 (2025), no. 5, e0321359, DOI 10.1371/journal.pone.0321359
Jyothish, K. and R. Santiago, Quantitative Structure–Property Relationship Modeling with the Prediction of Physicochemical Properties of Some Novel Duchenne Muscular Dystrophy Drugs. ACS omega. 10 (2025), no. 4, 3640, DOI 10.1021/acsomega.4c08572
Tiikkainen, P., L. Bellis, Y. Light and L. Franke, Estimating error rates in bioactivity databases. Journal of chemical information and modeling. 53 (2013), no. 10, 2499-2505, DOI 10.1021/ci400099q
Little, R. J. and D. B. Rubin, Statistical analysis with missing data; John Wiley & Sons, 2019. DOI: 10.1002/9781119482260.
Schneider, T., Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of climate. 14 (2001), no. 5, 853-871, DOI 10.1175/1520-0442(2001)014%3C0853:AOICDE%3E2.0.CO;2
Li, J., S. Guo, R. Ma, J. He, X. Zhang, D. Rui, Y. Ding, Y. Li, L. Jian and J. Cheng, Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets. BMC Medical Research Methodology. 24 (2024), no. 1, 41, DOI 10.1186/s12874-024-02173-x
Alwateer, M., E.-S. Atlam, M. M. Abd El-Raouf, O. A. Ghoneim and I. Gad, Missing data imputation: A comprehensive review. Journal of Computer and Communications. 12 (2024), no. 11, 53-75, DOI 10.4236/jcc.2024.1211004
Schneiderman, E. D., C. J. Kowalski and S. M. Willis, Regression imputation of missing values in longitudinal data sets. International journal of bio-medical computing. 32 (1993), no. 2, 121-133, DOI 10.1016/0020-7101(93)90051-7
Sun, Y., J. Li, Y. Xu, T. Zhang and X. Wang, Deep learning versus conventional methods for missing data imputation: A review and comparative study. Expert Systems with Applications. 227 (2023), no. 120201, DOI 10.1016/j.eswa.2023.120201
Allen, D. M., The relationship between variable selection and data agumentation and a method for prediction. technometrics. 16 (1974), no. 1, 125-127, DOI 10.1080/00401706.1974.10489157
Myers, R. H., Classical and modern regression with applications; Boston : PWS-Kent, 1990.
Golbraikh, A. and A. Tropsha, Beware of q2! Journal of molecular graphics and modelling. 20 (2002), no. 4, 269-276, DOI 10.1016/S1093-3263(01)00123-1
Roy, K., S. Kar and R. N. Das, Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment; Academic press, 2015.
Consonni, V., D. Ballabio and R. Todeschini, Evaluation of model predictive ability by external validation techniques. Journal of chemometrics. 24 (2010), no. 3‐4, 194-201, DOI 10.1002/cem.1290
Pavel, H., A. Roy, A. Santra and S. Chakravarthy. Degree centrality definition, and its computation for homogeneous multilayer networks using heuristics-based algorithms. In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, 2022; Springer: pp 28-52.
Mathad, V. and M. Pavithra, Closeness Centrality Weight and Edge Closeness Centrality Weight of Graphs. Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications & Algorithms. 32 (2025), no. 109-124, DOI
Sukumaran, S. and S. Unnithan, Mathematical Perspectives of Leverage Centrality: A Review. Indian Journal of Science and Technology. 16 (2023), no. 39, 3325-3331, DOI 10.17485/IJST/v16i39.1234
Berberler, M. E., Leverage centrality analysis of infrastructure networks. Numerical Methods for Partial Differential Equations. 37 (2021), no. 1, 767-781, DOI 10.1002/num.22551
Ortega, J. M. E. and R. G. Eballe, Harmonic Centrality and Centralization of Some Graph Products. arXiv preprint arXiv:2205.03791. (2022), no. DOI 10.9734/ARJOM/2022/v18i530377
Hage, P. and F. Harary, Eccentricity and centrality in networks. Social networks. 17 (1995), no. 1, 57-63, DOI 10.1016/0378-8733(94)00248-9
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