Articles in various scientific fields


  1. Simple general-model pseudopotential.
    Veljkovic V., Slavic I.
    Phys. Rev. Let., 1972;29:105.
  2. The dependence of the Fermi energy on the atomic number.
    Veljkovic V.
    Phys. Lett.1973;45A:41.
  3. General model pseudopotential for positive ions.
    Veljkovic V., Lalovic D.
    Phys. Lett. 1973;45A:59
  4. Superconductivity and the periodic system.
    Veljkovic V., Lalovic D.,
    Phys. Rev. 1975;11,4242
  5. Resistivity calculations for liquid metals.
    Vukajlovic F., Zekovic S., Veljkovic V.,
    Physica 1977;92B:66.
  6. The temperature dependence of the resistivity of liquid lead.
    Davidovic M., Vukajlovic F., Zekovic S., Veljkovic V.
    Phil. Mag 1977;36:1257.
  7. Application of the modified Veljković-Slavić pseudopotential for calculation of the electronic charge density of Si
    Maksimović DG, Mašović DR, Popović ZS
    Solid Stat Phys 1986;136:K111
  8. Local-pseudopotential calculation for optical properties and photoemission valence-band spectrum of silicon
    D. R. Masovic DR, Vukajlovic FR, Zekovic S
    Journal of Physics C Solid State Physics 1983;16(35):6731-6738
  9. Phonon spectra of alkali metals
    Zekovic S, Vukajlovic S, Veljkovic V.
    Physica B+C q1982;114,316-322.
  10. Model pseudopotential for elementary semiconductors
    Masovic DR, Zekovis S
    Phys Stat Sol B 1979;1:71
  11. A stoichiometry criterion for high Tc dsuperconductors
    Veljkovic V, Lalovic d.
    Phys Lett A 1989;A142:528.
  12. Energy transfer to the phonons of a macromolecule through light pumping.
    Faraji E, Franzosi R, Mancini S, Pettini M.
    Sci Rep 2021; 11, 6591.
  13. Transition between random and periodic electron currents on a DNA chain.
    Faraji E, Franzosi R, Mancini S, Pettini M.
    Int J Mol Sci 2021;22:7351.

Material Science

  1. Effect of alloying elements on the formation of boride layer on steel.
    Blazon M., Stanojevic B., Veljkovic V.
    Scr. Metall. 1975;9:1153.
  2. Chemical bond in intermetallic compounds and its influence on electrical properties.
    Veljkovic V., Tosic B., Janjic J.
    Scr. Metall. 1975, 9, 459.
  3. Ion-ion interaction and superconductivity of metals and intermetallic compounds.
    Veljkovic V., Janjic J., Tosic B. J.
    Mater. Sci. 1979;13:1138.

Nuclear Waste Managament and Radiation Protection

  1. Theoretical stability assessment of uranyl phosphates and apatites: selection of amendments for in situ remediation of uranium.
    Raicevic S., Wright J., Veljkovic V., Conca J.
    Sci Total Environ 2006;355:13.
  2. Theoretical assessment of phosphate amendments
    Raicevic S, Perovic V, Zouboulis AI.
    J Mathematical Chem. 2013;51:2238-55.
  3. Prediction of the weathering properties of minerals based on the ion-ion interaction potential
    Raicevic, S; Wright, JV; Vujic, J; Conca, JL.
    Mater Res Soc Symp proc 2004;824:455
  4. Peptide agonists of toll-like receptor 5 ligand and method of use
    Veljkovic V, Veljkovic n, Glisic S, Perovic V, Doliana R, Colombatti A.
    Patent: US20170051021A1

Biomedical Sciences

Infectious diseases

  1. Identification of nanopeptide from HTLV-III, ARV-2 and LAVBRU envelope gp120 determining binding to T4 cell surface protein.
    Veljković V, Metlas R.
    Cancer Biochem Biophys. 1988 Nov;10(2):91-106.
  2. Characterization of conserved properties of hemagglutinin of H5N1 and human influenza viruses: possible consequences for therapy and infection control.
    Veljkovic V, Veljkovic N, Muller CP, Müller S, Glisic S, Perovic V, Köhler H.
    BMC Struct Biol., 9, 21 (2009).
  3. Assessment of hepatitis C virus protein sequences with regard to interferon/ribavirin combination therapy response in patients with HCV genotype b.
    Glisic S, Veljkovic N, Jovanovic Cupic S, Vasiljevic N, Prljic J, Gemovic B, Perovic V, Veljkovic V.
    Protein J. 2012;31(2):129
  4. In silicoanalysis suggests interaction between Ebola virus and the extracellular matrix.
    Veljkovic V, Glisic S, Muller CP, Scotch M, Branch DR, Perovic VR, Sencanski M, Veljkovic N, Colombatti A.
    Front Microbiol. 2015;6:135.
  5. Zika virus infection elicits auto-antibodies to C1q.
    Koma T, Veljkovic V, Anderson DE, Wang LF, Rossi SL, Shan C, Shi PY, Beasley DW, Bukreyeva N, Smith JN, Hallam S, Huang C, von Messling V, Paessler S.
    Sci Rep. 2018;8:1882.
  6. Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target.
    Veljkovic V, Vergara-Alert J, Segalés J, Paessler S.
    F1000Res. 2020;9:52.
  7. In Silico Discovery of a Substituted 6-Methoxy-quinalidine with Leishmanicidal Activity in Leishmania infantum.
    Stevanović S, Perdih A, Senćanski M, Glišić S, Duarte M, Tomás AM, Sena FV, Sousa FM, Pereira MM, Solmajer T
    Molecules. 2018;23:772.
  8. Ehrlichia chaffeensis TRP120 Is a Wnt Ligand Mimetic That Interacts with Wnt Receptors and Contains a Novel Repetitive Short Linear Motif That Activates Wnt Signaling.
    Rogan MR, Patterson LL, Byerly CD, Luo T, Paessler S, Veljkovic V, Quade B, McBride JW.
    mSphere. 2021 Apr 21;:e00216-2
  9. EMILINs interact with anthrax protective antigen and Inhibit toxin action in vitro.
    Doliana R, Veljkovic V, Prljic J, Veljkovic N, De Lorenzo E, Mongiati M, Ligresti g, Marastoni S, Colombatti A. Matrix Biol. 2008;27:96.

Chronic diseases


  1. Physical activity and natural anti-VIP antibodies: potential role in breast and prostate cancer therapy.
    Veljkovic M, Dopsaj V, Dopsaj M, Branch DR, Veljkovic N, Sakarellos-Daitsiotis MM, Veljkovic V, Glisic S, Colombatti A.
    PLoS One. 2011;6:e28304.
  2. Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells.
    Srdic-Rajic T, Nikolic K, Cavic M, Djokic I, Gemovic B, Perovic V, Veljkovic N.
    Eur J Pharm Sci. 2016;81:172.
  3. Cytoplasmatic compartmentalization by Bcr-Abl promotes TET2 loss-of-function in chronic myeloid leukemia.
    Mancini M, Veljkovic N, Leo E, Aluigi M, Borsi E, Galloni C, Iacobucci I, Barbieri E, Santucci MA.J Cell Biochem. 2012;113:2765.
  4. 14-3-3 ligand prevents nuclear import of c-ABL protein in chronic myeloid leukemia.
    Mancini M, Veljkovic N, Corradi V, Zuffa E, Corrado P, Pagnotta E, Martinelli G, Barbieri E, Santucci MA.
    Traffic. 2009 Jun;10(6):637-47.
  5. A bioactive peptide analogue for myxoma virus protein with a targeted cytotoxicity for human skin cancer in vitro.
    Almansour NM, Pirogova E, Coloe PJ, Cosic I, Istivan TS.
    J Biomed Sci. 2012;19:65
  6. Biological effects of a de novo designed myxoma virus peptide analogue: evaluation of cytotoxicity on tumor cells.
    Istivan TS, Pirogova E, Gan E, Almansour NM, Coloe PJ, Cosic I.
    PLoS One. 2011;6:e24809.
  7. Investigating the interaction between oncogene and tumor suppressor protein.
    Pirogova E, Akay M, Cosic I.
    IEEE Trans Inf Technol Biomed. 2009;13:10.
  8. Identification of Homo sapiens cancer classes based on fusion of hidden gene features.
    Das J, Barman S.
    J Biomed Infor 2020;110:103555

Cardiovascular diseases

  1. Lipoprotein lipase: A bioinformatics criterion for assessment of mutations as a risk factor for cardiovascular disease
    Glisic S, Arrigo P, alavantic D, Perovic V, Prljic J, veljkovic N.
    Proteins 2008;70:855.
  2. In silico Therapeutics for Neurogenic Hypertension and Vasovagal Syncope
    Tijana Bojić, Vladimir R. Perović, Sanja Glišić
    Front Neurosci. 2015; 9: 52
  3. Influenza vaccine as prevention for cardiovascular diseases: possible molecular mechanism.
    Veljkovic V, Glisic S, Veljkovic N, Bojic T, Dietrich U, Perovic VR, Colombatti A. Vaccine. 2014;32:6569.
  4. Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3.
    Glišić S, Cavanaugh DP, Chittur KK, Sencanski M, Perovic V, Bojić T.
    BMC Bioinformatics. 2016;17:139

Drug discovery

  1. Application of the EIIP/ISM bioinformatics concept in development of new drugs.
    Veljkovic V., Veljkovic N., Este J., Huther A., Dietrich U.
    Curr. Medic. Chem. 2007;14: 441.
  2. Simple criterion for selection of flavonoid compounds with anti-HIV activity.
    Veljkovic V., J.F. Mouscadet, Veljkovic N., Glisic S., Debyser Z. Bioorg. Medic.
    Chem. Lett. 2007:17:1226.
  3. Novel virtual screening protocol based on the combined use of molecular modeling and electron-ion interaction potential techniques to design HIV-1 integrase inhibitors.
    Tintori C, Manetti F, Veljkovic N, Perovic V, Vercammen J, Hayes S, Massa S, Witvrow M, Debyser Z, Veljkoivic V, Botta M.
    J Chem Inf Model. 2007;47:1536.
  4. Discovery of new therapeutic targets by the informational spectrum method.
    Veljkovic N, Glisic S, Prljic J, Perovic V, Botta M, Veljkovic V.
    Curr Protein Pept Sci. 9, 493 (2008).
  5. New in silico and conventional in vitro approaches to advance HIV drug discovery and design.
    Maga G, Veljkovic N, Crespan E, Spadari S, Prljic J, Perovic V, Glisic S, Veljkovic V.
    Expert Opin Drug Discov. 2013;8:83.
  6. Improving attrition rates in Ebola virus drug discovery.
    Glisic S, Paessler S, Veljkovic N, Perovic VR, Prljic J, Veljkovic V.
    Expert Opin Drug Discov. 2015;10:1025.
  7. Virtual screening for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection,
    Veljkovic V, Loiseau PM, Figadere B, Glisic S, Veljkovic N, Perovic VR, Cavanaugh DP, Branch DR.
    F1000Res. 2015;4:34.
  8. Simple chemoinformatics criterion using electron donor-acceptor molecular characteristics for selection of antibiotics against multi-drug-resistant bacteria.
    Veljkovic V, Glisic S, Perovic V, Paessler S, Veljkovic N, Nicolson GL.
    Discoveries 2016; 4: e64.
  9. Biological activity of chemical compounds and their molecular structure-information approach.
    Mukhomorov VK.
    J. Chem. Eng. Chem. Res. 2014;1:54.
  10. Virtual Screen for Repurposing of Drugs for Candidate Influenza a M2 Ion-Channel Inhibitors.
    Radosevic D, Sencanski M, Perovic V, Veljkovic N, Prljic J, Veljkovic V, Mantlo E, Bukreyeva N, Paessler S, Glisic S.
    Front Cell Infect Microbiol. 2019;9:67.
  11. Drug Repurposing for Candidate SARS-CoV-2 Main Protease Inhibitors by a Novel In Silico Method.
    Sencanski M, Perovic V, Pajovic SB, Adzic M, Paessler S, Glisic S.
    Molecules 2020;25:3830.
  12. The role of long-range intermolecular interactions in discovery of new drugs.
    Veljkovic N, Glisic S, Perovic V, Veljkovic V.
    Exp. Opin. Drug Disc. 2011;6:1263.


  1. Protein and DNA sequence similarity between the V3 loop of HIV-1 envelope protein gp120 and immunoglobulin variable region.
    Metlas R., Veljkovic V., Paladini R., Pongor S.
    Biochem. Biophys. Res.Commun. 1991;179:1056.
  2. HIV and idiotypic T-cel regulation: another view.
    Veljkovic V., Metlas R.
    Immunol. Today 1992;15:39.
  3. Antibodies reactive with C-terminus of the second conserved region of HIV-1gp120 as possible prognostic marker and therapeutic agent for HIV disease.
    Veljkovic N., Metlas R., Prljic J., Manfredi R., Branch D., Stringer W. Veljkovic V.
    J. Clin. Virol. 2004;11:39.
  4. Application of VIP/NTM reactive natural antibodies in therapy of HIV disease.
    Veljkovic V., Metlas R.
    Int. Rev. Immunol. 2004;23:437.
  5. HIV-1 gp120 and immune network.
    Metlas R., Veljkovic V.
    Int. Rev. Immunol. 2004;23:413.
  6. Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic.
    Veljkovic V, Paessler S, Glisic S, Prljic J, Perovic VR, Veljkovic N, Scotch M.
    Front Microbiol. 2015;6:1456.
  7. Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019
    Slobodan Paessler, Veljko Veljkovic
    F1000Res. 2018;7:298.
  8. Antibody epitope specificity for dsDNA phosphate backbone is an intrinsic property of the heavy chain variable Ggermline gene segment used
    Tatjana Srdic-Rajic, Heinz Kohler, Vladimir Jurisic, Radmila Metlas
    Front Immunol. 2018; 9: 2378


  1. A simple method for calculation of basic molecular properties of nutrients and their use as a criterion for a healthy diet.
    Veljkovic V, Perovic V, Anderluh M, Paessler S, Veljkovic M, Glisic S, Nicolson G.
    F1000Res. 2017;6:13.


  1. The global average DNA base composition of coding regions may be determined by the electron-ion interaction potential.
    Lalović D, Veljković V.
    Biosystems. 1990;23:311
  2. EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection.
    He W, Jia C.
    Mol Biosyst. 2017;13:767.
  3. 4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction.
    He W, Jia C, Zou Q.
    Bioinformatics. 2019;35:593.
  4. Improved algorithm for analysis of DNA sequences using multiresolution transformation.
    Inbamalar TM, Sivakumar R.
    Scientific World J 2015;2015:786497.
  5. System identification: DNA computing approach.
    Huang CH, Jan HY, Lin CL, Lee CS.
    ISA Trans. 2009;48:254.
  6. A fast algorithm for exonic regions prediction in DNA sequences.
    Saberkari H, Shamsi M, Heravi H, Sedaaghi MH.
    J Med Signals Sens. 2013;3:139.
  7. Application of artificial neural networks for prokaryotic transcription terminator prediction.
    Nair TM, Tambe SS, Kulkarni BD.
    FEBS Lett. 1994;346:273.
  8. Performance Improvement of the Goertzel Algorithm in Estimating of Protein Coding Regions Using Modified Anti-notch Filter and Linear Predictive Coding Model.
    Farsani MS, Sahhaf MR, Abootalebi V.J
    Med Signals Sens. 2016;6:130.
  9. Comparison of Numerical Representations of Genomic Sequences: Choosing the Best Mapping for Wavelet Analysis.
    Saini S, Dewan L.
    Int. J. Appl. Comput. Math. 2017;3:2943.

Protein research

  1. A novel method for achieving an optimal classification of the proteinogenic amino acids.
    Then A, Mácha K, Ibrahim B, Schuster S.
    Sci Rep. 2020;10:15321.
  2. An Integrated-OFFT Model for the Prediction of Protein Secondary Structure Class.
    Panda B, Majhi B, Thakur A.
    Curr Comput Aided Drug Des. 2019;15:45.
  3. CMDWave: conserved motifs detection using wavelets.
    Riaz T, Li KB, Tang F, Krishnan A.
    In Silico Biol. 2005;5:415.
  4. SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method.
    Deng SP, Huang DS.
    Methods. 2014;69:207.
  5. Selection of amino acid parameters for Fourier transform-based analysis of proteins.
    Lazović J.
    Comput Appl Biosci. 1996 Dec;12:553.
  6. Protein structure analysis using the resonant recognition model and wavelet transforms.
    Fang Q, Cosic I.
    Australas Phys Eng Sci Med. 1998;21:179.
  7. Application of fourier transform and proteochemometrics principles to protein engineering.
    Cadet F, Fontaine N, Vetrivel I, Chong MNF, SavriamaO, Cadet X, Charton P.
    BMC Bioinformatics 2018;19:382
  8. Genomics and proteomics: A signal processor’s tour.
    Vaidyanathan PP.
    IEEE Circuits Syst Mag, 2004;4:6.
  9. Bioactive peptide design using the Resonant Recognition Model
    Cposic I, Pirogova E.
    Nonlinear Biomed Phys 2007; 1 7.