Skip to main content

Advertisement

SpringerLink
Log in

QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum

  • Original Paper
  • Published:
Chemical Papers Aims and scope Submit manuscript

Abstract

A data set of amidino bis-benzimidazoles, in particular 2′-arylsubstituted-1H,1′H-[2,5′]bisbenzimidazolyl-5-carboximidine derivatives with anti-malarial activity against Plasmodium falciparum was employed in investigating the quantitative structure-activity relationship (QSAR). Quantum chemical and molecular descriptors were obtained from B3LYP/6-31g(d) calculations and Dragon software, respectively. Significant variables, which included total energy (E T), highest occupied molecular orbital (HOMO), Moran autocorrelation-lag3/weighted by atomic masses (MATS3m), Geary autocorrelation-lag8/weighted by atomic masses (GATS8m), and 3D-MoRSEsignal 11/weighted by atomic Sanderson electronegativities (Mor11e), were used in the construction of QSAR models using multiple linear regression (MLR) and artificial neural network (ANN). The results indicated that the predictive models for both the MLR and ANN approaches using leave-one-out cross-validation afforded a good performance in modelling the anti-malarial activity against P. falciparum as observed by correlation coefficients of leave-one-out cross-validation (R LOO-CV) of 0.9760 and 0.9821, respectively, root mean squared error of leave-one-out cross-validation (RMSELOO-CV) of 0.1301 and 0.1102, respectively, and predictivity of leave-one-out cross-validation (Q 2LOO-CV ) of 0.9526 and 0.9645, respectively. Model validation was performed using an external testing set and the results suggested that the model provided good predictivity for both MLR and ANN models with correlation coefficient of the external set (R Ext) values of 0.9978 and 0.9844, respectively, root mean squared error of the external set (RMSEExt) of 0.0764 and 0.1302 respectively, and predictivity of the external set (Q 2Ext ) of 0.9956 and 0.9690, respectively. Furthermore, the robustness of the QSAR models is corroborated by a number of statistical parameters, comprising adjusted correlation coefficient (R 2Adj ), standard deviation (s), predicted residual sum of squares (PRESS), standard error of prediction (SDEP), total sum of squares deviation (SSY), and quality factor (Q). The QSAR models so constructed provide pertinent insights for the future design of anti-malarial agents.

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.

Similar content being viewed by others

References

  • Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi-Markopoulou, O. (2006). A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes. Bioorganic & Medicinal Chemistry, 14, 6686–6694. DOI: 10.1016/j.bmc.2006.05.061.

    Article  CAS  Google Scholar 

  • Alp, M., Göker, H., Brun, R., & Yıldız, S. (2009). Synthesis and antiparasitic and antifungal evaluation of 2′-arylsubstituted-1H,1′H-[2,5′]bisbenzimidazolyl-5-carboxamidines. European Journal of Medicinal Chemistry, 44, 2002–2008. DOI: 10.1016/j.ejmech.2008.10.003.

    Article  CAS  Google Scholar 

  • Alves, C. N., Pinheiro, J. C., Camargo, A. J., Ferreira, M. M. C., Romero, R. A. F., & da Silva, A. B. F. (2001). A multiple linear regression and partial least squares study of flavonoid compounds with anti-HIV activity. Journal of Molecular Structure: THEOCHEM, 541, 81–88. DOI: 10.1016/s0166-1280(00)00755-7.

    Article  CAS  Google Scholar 

  • Bloland, P. B. (2001). Drug resistance in malaria. Retrieved November 25, 2011, from http://www.who.int/csr/resources/publications/drugresist/malaria.pdf

    Google Scholar 

  • Caballero, J., & Fernández, M. (2006). Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. Journal of Molecular Modeling, 12, 168–181. DOI: 10.1007/s00894-005-0014-x.

    Article  CAS  Google Scholar 

  • Cao, D. S., Liang, Y. Z., Xu, Q. S., Li, H. D., & Chen, X. (2010). A new strategy of outlier detection for QSAR/QSPR. Journal of Computational Chemistry, 31, 592–602. DOI: 10.1002/jcc.21351.

    CAS  Google Scholar 

  • Castillo-Garit, J. A., Marrero-Ponce, Y., Escobar, J., Torrens, F., & Rotondo, R. (2008). A novel approach to predict aquatic toxicity from molecular structure. Chemosphere, 73, 415–427. DOI: 10.1016/j.chemosphere.2008.05.024.

    Article  CAS  Google Scholar 

  • Del Poeta, M., Schell, W. A., Dykstra, C. C., Jones, S. K., Tidwell, R. R., Kumar, A., Boykin, D. W., & Perfect, J. R. (1998). In vitro antifungal activities of a series of dicationsubstituted carbazoles, furans, and benzimidazoles. Antimicrobial Agents and Chemotherapy, 42, 2503–2510.

    Google Scholar 

  • Dennington, R., II, Keith, T., Millam, J., Eppinnett, K., Hovell, W. L., & Gilliland, R. (2003). GaussView, Version 3.09 [computer software]. Shawnee Mission, KS, USA: Semichem.

    Google Scholar 

  • Durand, A. C., Farce, A., Carato, P., Dilly, S., Yous, S., Berthelot, P., & Chavatte, P. (2007). Quantitative structureactivity relationships studies of antioxidant hexahydropyridoindoles and flavonoid derivatives. Journal of Enzyme Inhibition and Medicinal Chemistry, 22, 556–562. DOI: 10.1080/14756360701425238.

    Article  CAS  Google Scholar 

  • Farooq, U., & Mahajan, R. C. (2004). Drug resistance in malaria. Journal of Vector Borne Diseases, 41, 45–53.

    Google Scholar 

  • Fernández, M., Caballero, J., Helguera, A. M., Castro, E. A., & González, M. P. (2005). Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. Bioorganic & Medicinal Chemistry, 13, 3269–3277. DOI: 10.1016/j.bmc.2005.02.038.

    Article  Google Scholar 

  • Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Montgomery, J. A., Jr., Vreven, T., Kudin, K. N., Burant, J. C., Millam, J. M., Iyengar, S. S., Tomasi, J., Barone, V., Mennucci, B., Cossi, M., Scalmani, G., Rega, N., Petersson, G. A., Nakatsuji, H., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Klene, M., Li, X., Knox, J. E., Hratchian, H. P., Cross, J. B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R. E., Yazyev, O., Austin, A. J., Cammi, R., Pomelli, C., Ochterski, J. W., Ayala, P. Y., Morokuma, K., Voth, G. A., Salvador, P., Dannenberg, J. J., Zakrzewski, V. G., Dapprich, S., Daniels, A. D., Strain, M. C., Farkas, O., Malick, D. K., Rabuck, A. D., Raghavachari, K., Foresman, J. B., Ortiz, J. V., Cui, Q., Baboul, A. G., Clifford, S., Cioslowski, J., Stefanov, B. B., Liu, G., Liashenko, A., Piskorz, P., Komaromi, I., Martin, R. L., Fox, D. J., Keith, T., Al-Laham, M. A., Peng, C. Y., Nanayakkara, A., Challacombe, M., Gill, P. M. W., Johnson, B., Chen, W., Wong, M. W., Gonzalez, C., & Pople, J. A. (2004). Gaussian 03, Revision C.02 [computer software]. Wallingford, CT, USA: Gaussian.

    Google Scholar 

  • García, J., Duchowicz, P. R., Rozas, M. F., Caram, J. A., Mirífico, M. V., Fernández, F. M., & Castro, E. A. (2011). A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. Journal of Molecular Graphics and Modelling, 31, 10–19. DOI: 10.1016/j.jmgm.2011.07.007.

    Article  Google Scholar 

  • Gogtay, N. J., Kshirsagar, N. A., & Vaidya, A. B. (2006). Current challenges in drug-resistant malaria. Journal of Postgraduate Medicine, 52, 241–242.

    CAS  Google Scholar 

  • Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science, 26, 694–701. DOI: 10.1002/qsar.200610151.

    Article  CAS  Google Scholar 

  • Greenwood, B. M., Bojang, K., Whitty, C. J., & Targett, G. A. (2005). Malaria. Lancet, 365, 1487–1498. DOI: 10.1016/s0140-6736(05)66420-3.

    Article  CAS  Google Scholar 

  • Gupta, L., Patel, A., Karthikeyan, C., & Trivedi, P. (2010). QSAR studies on dihydro-alkoxy-benzyl-oxopyrimidines (DABOs) derivatives, a new series of potent, broad-spectrum non-nucleoside reverse transcriptase inhibitors. Journal of Current Pharmaceutical Research, 1, 19–25.

    Google Scholar 

  • Idro, R., Jenkins, N. E., & Newton, C. R. J. C. (2005). Pathogenesis, clinical features, and neurological outcome of cerebral malaria. Lancet Neurology, 4, 827–840. DOI: 10.1016/s1474-4422(05)70247-7.

    Article  Google Scholar 

  • Idro, R., Marsh, K., John, C. C., & Newton, C. R. J. (2010). Cerebral malaria: Mechanisms of brain injury and strategies for improved neurocognitive outcome. Pediatric Research, 68, 267–274. DOI: 10.1203/pdr.0b013e3181eee738.

    Article  Google Scholar 

  • Jain, A. K., Veerasamy, R., Vaidya, A., Kashaw, S., Mourya, V. K., & Agrawal, R. K. (2012). QSAR analysis of B-ring-modified diaryl ether derivatives as a InhA inhibitors. Medicinal Chemistry Research, 21, 145–151. DOI: 10.1007/s00044-010-9518-8.

    Article  CAS  Google Scholar 

  • Jalali-Heravi, M., & Parastar, F. (2000). Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. Journal of Chemical Information and Modeling, 40, 147–154. DOI: 10.1021/ci990314+.

    Article  CAS  Google Scholar 

  • Karelson, M., Lobanov, V. S., & Katritzky, A. R. (1996). Quantum-chemical descriptors in QSAR/QSPR studies. Chemical Reviews, 96, 1027–1044. DOI: 10.1021/cr950202r.

    Article  CAS  Google Scholar 

  • Khosrokhavar, R., Ghasemi, J. B., & Shiri, F. (2010). 2D quantitative structure-property relationship study of mycotoxins by multiple linear regression and support vector machine. International Journal of Molecular Sciences, 11, 3052–3068. DOI: 10.3390/ijms11093052.

    Article  CAS  Google Scholar 

  • Kshirsagar, N. A. (2006). Malaria: Antimalarial resistance and policy ramifications and challenges. Journal of Postgraduate Medicine, 52, 291–293.

    CAS  Google Scholar 

  • Nantasenamat, C., Naenna, T., Isarankura Na Ayudhya, C., & Prachayasittikul, V. (2005). Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. Journal of Computer-Aided Molecular Design, 19, 509–524. DOI: 10.1007/s10822-005-9004-4.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Tansila, N., Naenna, T., & Prachayasittikul, V. (2007). Prediction of GFP spectral properties using artificial neural network. Journal of Computational Chemistry, 28, 1275–1289. DOI: 10.1002/jcc.20656.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2008). Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. Journal of Molecular Graphics and Modelling, 27, 188–196. DOI: 10.1016/j.jmgm.2008.04.005.

    Article  CAS  Google Scholar 

  • Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2009). A practical overview of quantitative structure-activity relationship. EXCLI Journal, 8, 74–88.

    Google Scholar 

  • Nwaka, S., & Ridley, R. G. (2003). Virtual drug discovery and development for neglected diseases through public-private partnerships. Nature Reviews Drug Discovery, 2, 919–928. DOI: 10.1038/nrd1230.

    Article  CAS  Google Scholar 

  • Nwaka, S., & Hudson, A. (2006). Innovative lead discovery strategies for tropical diseases. Nature Reviews Drug Discovery, 5, 941–955. DOI: 10.1038/nrd2144.

    Article  CAS  Google Scholar 

  • Papa, E., Villa, F., & Gramatica, P. (2005). Statistically validated QSARs, beased on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow). Journal of Chemical Information and Modeling, 45, 1256–1266. DOI: 10.1021/ci050212l.

    Article  CAS  Google Scholar 

  • Parr, R. G., Donnelly, R. A., Levy, M., & Palke, W. E. (1978). Electronegativity: The density functional viewpoint. Journal of Chemical Physics, 68, 3801. DOI: 10.1063/1.436185.

    Article  CAS  Google Scholar 

  • Parr, R. G., & Pearson, R. G. (1983). Absolute hardness: companion parameter to absolute electronegativity. Journal of the American Chemical Society, 105, 7512–7516. DOI: 10.1021/ja00364a005.

    Article  CAS  Google Scholar 

  • Parr, R. G., Szentpály, L., & Liu, S. (1999). Electrophilicity index. Journal of the American Chemical Society, 121, 1922–1924. DOI: 10.1021/ja983494x.

    Article  CAS  Google Scholar 

  • Podunavac-Kuzmanović, S. O., & Cvetković, D. D. (2011). QSAR modeling of antibacterial activity of some benzimidazole derivatives. Chemical Industry & Chemical Engineering Quarterly, 17, 33–38. DOI: 10.2298/ciceq100405050p.

    Article  Google Scholar 

  • Prachayasittikul, S., Wongsawatkul, O., Worachartcheewan, A., Nantasenamat, C., Ruchirawat, S., & Prachayasittikul, V. (2010). Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives. Molecules, 15, 198–214. DOI: 10.3390/molecules15010198.

    Article  CAS  Google Scholar 

  • Prasad, Y. R., Kumar, P. R., Smiles, D. J., & Babu, P. A. (2008). QSAR studies on chalcone derivatives as antibacterial agents against Bacillus pumilis. ARKIVOC, 11, 266–276.

    Article  Google Scholar 

  • Rastija, V., & Medić-Šarić, M. (2009). QSAR modeling of anthocyanins, anthocyanidins and catechins as inhibitors of lipid peroxidation using three-dimensional descriptors. Medicinal Chemistry Research, 18, 579–588. DOI: 10.1007/s00044-008-9151-y.

    Article  CAS  Google Scholar 

  • Saghaie, L., Sakhi, H., Sabzyan, H., Shahlaei, M., & Shamshirian, D. (2013). Stepwise MLR and PCR QSAR study of the pharmaceutical activities of antimalarial 3-hydroxypyridinone agents using B3LYP/6-311++G** descriptors. Medicinal Chemistry Research, 22, 1679–1688. DOI: 10.1007/s00044-012-0152-5.

    Article  CAS  Google Scholar 

  • Sawant, R. L., Bansode, C. A., & Wadekar, J. B. (2013). In vitro anti-inflammatory potential and QSAR analysis of oxazolo/thiazolo pyrimidine derivatives. Medicinal Chemistry Research, 22, 1884–1892. DOI: 10.1007/s00044-012-0189-5.

    Article  CAS  Google Scholar 

  • Shahlaei, M., Fassihi, A., & Nezami, A. (2009). QSAR study of some 5-methyl/trifluoromethoxy-1H-indole-2,3-dione-3-thiosemicarbazone derivatives as anti-tubercular agents. Research in Pharmaceutical Sciences, 4, 123–131.

    CAS  Google Scholar 

  • Suksrichavalit, T., Prachayasittikul, S., Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). Copper complexes of pyridine derivatives with superoxide scavenging and antimicrobial activities. European Journal of Medicinal Chemistry, 44, 3259–3265. DOI: 10.1016/j.ejmech.2009.03.033.

    Article  CAS  Google Scholar 

  • Sun, M., Zheng, Y. G., Wei, H. T., Chen, J. Q., & Ji, M. (2009). QSAR studies on 4-anilino-3-quinolinecarbonitriles as Src kinase inhibitors using robust PCA and both linear and nonlinear models. Journal of Enzyme Inhibition and Medicinal Chemistry, 24, 1109–1116. DOI: 10.1080/14756360802632906.

    Article  CAS  Google Scholar 

  • Suvannang, N., Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2011). Molecular docking of aromatase inhibitors. Molecules, 16, 3597–3617. DOI: 10.3390/molecules16053597.

    Article  CAS  Google Scholar 

  • Talete (2007). Dragon for windows (software for molecular descriptor calculations), version 5.5 [computer software]. Milano, Italy: Talete.

    Google Scholar 

  • Tanious, F. A., Hamelberg, D., Bailly, C., Czarny, A., Boykin, D. W., & Wilson, W. D. (2004). DNA sequence dependent monomer-dimer binding modulation of asymmetric benzimidazole derivatives. Journal of the American Chemical Society, 126, 143–153. DOI: 10.1021/ja030403+.

    Article  CAS  Google Scholar 

  • Thanikaivelan, P., Subramanian, V., Raghava Rao, J., & Unni Nair, B. (2000). Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chemical Physics Letters, 323, 59–70. DOI: 10.1016/s0009-2614(00)00488-7.

    Article  CAS  Google Scholar 

  • Thippakorn, C., Suksrichavalit, T., Nantasenamat, C., Tantimongcolwat, T., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2009). Modeling the LPS neutralization activity of anti-endotoxins. Molecules, 14, 1869–1888. DOI: 10.3390/molecules14051869.

    Article  CAS  Google Scholar 

  • Vahdani, S., & Bayat, Z. (2011). A quantitative structure-activity relationship (QSAR) study of anti-cancer drugs. Der Chemica Sinica, 2, 235–243.

    CAS  Google Scholar 

  • Veerasamy, R., Ravichandran, S., Jain, A., Rajak, H., & Agrawal, R. K. (2009). QSAR studies on novel anti-HIV agents using FA-MLR, FA-PLS and PCRA techniques. Digest Journal of Nanomaterials and Biostructures, 4, 823–834.

    Google Scholar 

  • Verma, R. P. (2006). Anti-cancer activities of 1,4-naphthoquinones: a QSAR study. Anti-Cancer Agents in Medicinal Chemistry, 6, 489–499. DOI: 10.2174/187152006778226512.

    Article  CAS  Google Scholar 

  • Verma, R. P., & Hansch, C. (2010). QSAR modeling of taxane analogues against colon cancer. European Journal of Medicinal Chemistry, 45, 1470–1477. DOI: 10.1016/j.ejmech.2009.12.054.

    Article  CAS  Google Scholar 

  • Vlaia, V., Olariu, T., Vlaia, L., Butur, M., Ciubotariu, C., Medeleanu, M., & Ciubotariu, D. (2009). Quantitative structure-activity relationship (QSAR). V. Analysis of the toxicity of aliphatic esters by means of molecular compressibility descriptors. Farmacia, 57, 549–561.

    CAS  Google Scholar 

  • Whitley, D. C., Ford, M. G., & Livingstone, D. J. (2000). Unsupervised forward selection: A method for eliminating redundant variables. Journal of Chemical Information and Modeling, 40, 1160–1168. DOI: 10.1021/ci000384c.

    Article  CAS  Google Scholar 

  • White, N. J. (2004). Antimalarial drug resistance. Journal of Clinical Investigation, 113, 1084–1092. DOI: 10.1172/jci200421682.

    CAS  Google Scholar 

  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: practical machine learning tools and techniques (3rd ed.). San Francisco, CA, USA: Morgan Kaufmann.

    Google Scholar 

  • Worachartcheewan, A., Nantasenamat, C., Naenna, T., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2009). Modeling the activity of furin inhibitors using artificial neural network. European Journal of Medicinal Chemistry, 44, 1664–1673. DOI: 10.1016/j.ejmech.2008.09.028.

    Article  CAS  Google Scholar 

  • Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, S., & Prachayasittikul, V. (2011). Predicting the free radical scavenging activity of curcumin derivatives. Chemometrics and Intelligent Laboratory Systems, 109, 207–216. DOI: 10.1016/j.chemolab.2011.09.010.

    Article  CAS  Google Scholar 

  • Worachartcheewan, A., Prachayasittikul, S., Pingaew, R., Nantasenamat, C., Tantimongcolwat, T., Ruchirawat, S., & Prachayasittikul, V. (2012). Antioxidant, cytotoxicity, and QSAR study of 1-adamantylthio derivatives of 3-picoline and phenylpyridines. Medicinal Chemistry Research, 21, 3514–3522. DOI: 10.1007/s00044-011-9903-y.

    Article  CAS  Google Scholar 

  • Yan, X. F. (2011). Multivariate outlier detection based on selforganizing map and adaptive nonlinear map and its application. Chemometrics and Intelligent Laboratory Systems, 107, 251–257. DOI: 10.1016/j.chemolab.2011.04.007.

    Article  CAS  Google Scholar 

  • Zahouily, M., Lazar, M., Elmakssoudi, A., Rakik, J., Elaychi, S., & Rayadh, A. (2006). QSAR for anti-malarial activity of 2-aziridinyl and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives. Journal of Molecular Modeling, 12, 398–405. DOI: 10.1007/s00894-005-0059-x.

    Article  CAS  Google Scholar 

  • Zhang, L., Zhu, H., Oprea, T. I., Golbraikh, A., & Tropsha, A. (2008). QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharmaceutical Research, 25, 1902–1914. DOI: 10.1007/s11095-008-9609-0.

    Article  CAS  Google Scholar 

  • Zou, C., & Zhou, L. (2007). QSAR study of oxazolidinone antibacterial agents using artificial neural networks. Molecular Simulation, 33, 517–530. DOI: 10.1080/08927020601188528.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Data Mining and Biomedical Informatics, Mahidol University, 10700, Bangkok, Thailand

    Apilak Worachartcheewan & Chanin Nantasenamat

  2. Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand

    Apilak Worachartcheewan, Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya & Virapong Prachayasittikul

Authors
  1. Apilak Worachartcheewan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chanin Nantasenamat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chartchalerm Isarankura-Na-Ayudhya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Virapong Prachayasittikul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Chanin Nantasenamat or Virapong Prachayasittikul.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C. et al. QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum . Chem. Pap. 67, 1462–1473 (2013). https://doi.org/10.2478/s11696-013-0398-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11696-013-0398-5

Keywords

Search

Navigation