Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening

References

• World Health Organization (WHO), 2016.
   Google Scholar
• Feder AF, Rhee S-Y, Holmes SP, et al. More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1. Elife 2016;5:e10670.
   PubMed Web of Science ®Google Scholar
• Struble K, Murray J, Cheng B, et al. Antiretroviral therapies for treatment-experienced patients: current status and research challenges. Aids 2005;19:747–756.
   PubMed Web of Science ®Google Scholar
• Opar A. New HIV drug classes on the horizon. Nat Rev Drug Discov. 2007;6:258–259.
   PubMed Web of Science ®Google Scholar
• Yilmaz NK, Schiffer CA. (2017). Drug resistance to HIV-1 protease inhibitors: molecular mechanisms and substrate coevolution. Antimicrobial Drug Resistance. Springer International Publishing AG; p. 535–544.
   Google Scholar
• De Clercq E. Toward improved anti-HIV chemotherapy: therapeutic strategies for intervention with HIV infections. J Med Chem. 1995;38:2491–2517.
   PubMed Web of Science ®Google Scholar
• Esposito F, Corona A, Tramontano E. HIV-1 reverse transcriptase still remains a new drug target: structure, function, classical inhibitors, and new inhibitors with innovative mechanisms of actions. Mol Biol Int. 2012;2012:1–23.
   Google Scholar
• Pandey AK, Dixit U, Kholodovych V, et al. The β1′− β2′ motif of the RNase H domain of human immunodeficiency virus type 1 reverse transcriptase is responsible for conferring open conformation to the p66 subunit by displacing the connection domain from the polymerase cleft. Biochemistry 2017;56:3434–3442.
   PubMed Web of Science ®Google Scholar
• Lin Y, Liu X, Yan R, et al. Synthesis and anti-HIV evaluation of novel 1, 3-disubstituted thieno [3, 2-c][1, 2, 6] thiadiazin-4 (3H)-one 2, 2-dioxides (TTDDs). Bioorganic Med Chem. 2008;16:157–163.
   PubMed Web of Science ®Google Scholar
• Ren J, Esnouf R, Garman E, et al. High resolution structures of HIV-1 RT from four RT–inhibitor complexes. Nat Struct Mol Biol. 1995;2:293–302.
   Web of Science ®Google Scholar
• Sluis-Cremer N, Tachedjian G. Mechanisms of inhibition of HIV replication by non-nucleoside reverse transcriptase inhibitors. Virus Res. 2008;134:147–156.
   PubMed Web of Science ®Google Scholar
• Wenthur CJ, Gentry PR, Mathews TP, et al. Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol. 2014;54:165–184.
   PubMed Web of Science ®Google Scholar
• Seckler JM, Barkley MD, Wintrode PL. Allosteric suppression of HIV-1 reverse transcriptase structural dynamics upon inhibitor binding. Biophysical J. 2011;100:144–153.
   PubMed Web of Science ®Google Scholar
• Sharaf NG, Xi Z, Ishima R, et al. The HIV‐1 p66 homodimeric RT exhibits different conformations in the binding‐competent and‐incompetent NNRTI site. Proteins: Structure Function Bioinform. 2017;85:2191–2197.
   PubMed Web of Science ®Google Scholar
• De Clercq E. Emerging anti-HIV drugs. Expert Opin Emerg Drugs. 2005;10:241–274.
   PubMedGoogle Scholar
• Poongavanam V, Namasivayam V, Vanangamudi M, et al. Integrative approaches in HIV‐1 non‐nucleoside reverse transcriptase inhibitor design. Wiley Interdiscip Rev Comput Mol Sci. 2017:e1328.
   Web of Science ®Google Scholar
• Monforte A-M, Logoteta P, Ferro S, D, Luca L, et al. Design, synthesis, and structure–activity relationships of 1, 3-dihydrobenzimidazol-2-one analogues as anti-HIV agents. Bioorganic Med Chem. 2009;17:5962–5967.
   PubMed Web of Science ®Google Scholar
• Ghasemi JB, Shiri F, Pirhadi S, et al. Discovery of new potential antimalarial compounds using virtual screening of ZINC database. CCHTS. 2015;18:227–234.
   Web of Science ®Google Scholar
• Zhang W. Computer-aided drug discovery. New York: Springer; 2016.
   Google Scholar
• Moffat JG, Vincent F, Lee JA, et al. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov. 2017;16:531–543.
   PubMed Web of Science ®Google Scholar
• Kumar A, Roy S, Tripathi S, et al. Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis. J Biomol Struct Dyn. 2016;34:239–249.
   PubMed Web of Science ®Google Scholar
• Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007;26:694–701.
   Google Scholar
• Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29:476–488.
   PubMed Web of Science ®Google Scholar
• Yousefinejad S, Hemmateenejad B. Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom Intell Lab Syst. 2015;149:177–204.
   Web of Science ®Google Scholar
• Lill MA. Multi-dimensional QSAR in drug discovery. Drug Discov Today. 2007;12:1013–1017.
   PubMed Web of Science ®Google Scholar
• Lavecchia A, Di Giovanni C. Virtual screening strategies in drug discovery: a critical review. CMC. 2013;20:2839–2860.
   PubMed Web of Science ®Google Scholar
• Ghosh S, Nie A, An J, et al. Structure-based virtual screening of chemical libraries for drug discovery. Curr Opin Chem Biol. 2006;10:194–202.
   PubMed Web of Science ®Google Scholar
• Yilmaz H, Ahmed L, Rasulev B, et al. Application of ligand-and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives. J Nanopart Res. 2016;18:123.
   Web of Science ®Google Scholar
• Chang C-K, Jeyachandran S, Hu N-J, et al. Structure-based virtual screening and experimental validation of the discovery of inhibitors targeted towards the human coronavirus nucleocapsid protein. Mol BioSyst. 2016;12:59–66.
   PubMed Web of Science ®Google Scholar
• Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3:935–949.
   PubMed Web of Science ®Google Scholar
• Akbar R, Jusoh SA, Amaro RE, et al. ENRI: A tool for selecting structure‐based virtual screening target conformations. Chem Biol Drug Des. 2017;89:762–771.
   PubMed Web of Science ®Google Scholar
• Plewczynski D, Spieser SA, Koch U. Assessing different classification methods for virtual screening. J Chem Inf Model. 2006;46:1098–1106.
   PubMed Web of Science ®Google Scholar
• Al Sharif M, Alov P, Vitcheva V, et al. Natural modulators of nonalcoholic fatty liver disease: Mode of action analysis and in silico ADME-Tox prediction. Toxicol Appl Pharmacol. 2017;337:45–66.
   PubMed Web of Science ®Google Scholar
• Alqahtani S. In silico ADME-Tox modeling: progress and prospects. Expert Opin Drug Metabol Toxicol. 2017;13:1147–1158.
   PubMed Web of Science ®Google Scholar
• Scala A, Rescifina A, Micale N, et al. Ensemble-based ADME‐Tox profiling and virtual screening for the discovery of new inhibitors of the Leishmania mexicana cysteine protease CPB2.8ΔCTE. Chem Biol Drug Design. 2017 [Nov 10]. doi: 10.1111/cbdd.13124
   PubMed Web of Science ®Google Scholar
• Czermiński R, Yasri A, Hartsough D. Use of support vector machine in pattern classification: application to QSAR studies. Mol Inform. 2001;20:227–240.
   Google Scholar
• Lei T, Sun H, Kang Y, et al. ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches. Mol Pharm. 2017;14:3935–3953.
   PubMed Web of Science ®Google Scholar
• Rensi SE, Altman RB. Shallow representation learning via Kernel PCA improves QSAR modelability. J Chem Inf Model. 2017;57:1859–1867.
   PubMed Web of Science ®Google Scholar
• Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods. 2000;44:235–249.
   PubMed Web of Science ®Google Scholar
• Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Rev. 1997;23:3–25.
   Web of Science ®Google Scholar
• Pirhadi S, Ghasemi JB. Pharmacophore identification, molecular docking, virtual screening, and in silico ADME studies of non‐nucleoside reverse transcriptase inhibitors. Mol Inf. 2012;31:856–866.
   PubMed Web of Science ®Google Scholar
• Kennard RW, Stone LA. Computer aided design of experiments. Technometrics 1969;11:137–148.
   Web of Science ®Google Scholar
• Yap CW. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32:1466–1474.
   PubMed Web of Science ®Google Scholar
• Forrest S. Genetic algorithms: principles of natural selection applied to computation. Science. 1993;261:872–878.
   PubMed Web of Science ®Google Scholar
• Holland JH. Genetic algorithms. Sci Am. 1992;267:66–72.
   PubMed Web of Science ®Google Scholar
• Leardi R, Gonzalez AL. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Syst. 1998;41:195–207.
   Web of Science ®Google Scholar
• Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J Chemometrics. 2000;14:643–655.
   Web of Science ®Google Scholar
• Mercader AG, Duchowicz PR, Fernández FM, et al. Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories. Chemom Intell Lab Syst. 2008;92:138–144.
   Web of Science ®Google Scholar
• Mercader AG, Duchowicz PR, Fernández FM, et al. Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories. J Chem Inf Model. 2010;50:1542–1548.
   PubMed Web of Science ®Google Scholar
• Mercader AG, Duchowicz PR, Fernández FM, et al. Advances in the replacement and enhanced replacement method in QSAR and QSPR theories. J Chem Inf Model. 2011;51:1575–1581.
   PubMed Web of Science ®Google Scholar
• Zhang L, Fourches D, Sedykh A, et al. The Discovery of Novel Antimalarial Compounds Enabled by QSAR-based Virtual Screening. J Chem Inf Model. 2013;53:475.
   PubMed Web of Science ®Google Scholar
• Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–843.
   PubMed Web of Science ®Google Scholar
• Zou KH, O’Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115:654–657.
   PubMed Web of Science ®Google Scholar
• Zhu W, Zeng N, Wang N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proceedings: Health Care and Life Sciences, Baltimore, Maryland. p. 1–9.
   Google Scholar
• Krueger BA, Weil T, Schneider G. Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists. J Computer Aided Mol Design. 2009;23:869.
   PubMed Web of Science ®Google Scholar
• Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure. 1975;405:442–451.
   PubMed Web of Science ®Google Scholar
• Cortes C, Vapnik V. Mach. Learn; 1995.
   Google Scholar
• Trotter MW, Buxton BF, Holden SB. Support vector machines in combinatorial chemistry. Measurement Control. 2001;34:235–239.
   Web of Science ®Google Scholar
• Wise B. PLS toolbox tutorial: Matlab version 6. Seattle (WA): Eigenvector Research; 2000.
   Google Scholar
• Matlab. Matlab V. 7.11. 0. The MathWorks Inc., Natick, MA; 2010.
   Google Scholar
• Sanders MP, McGuire R, Roumen L, et al. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Commun. 2012;3:28–38.
   Web of Science ®Google Scholar
• Ebalunode JO, Ouyang Z, Liang J, et al. Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques. J Chem Inform Model. 2008;48:889–901.
   PubMed Web of Science ®Google Scholar
• Sunseri J, Koes DR. Pharmit: interactive exploration of chemical space. Nucleic Acids Res. 2016;44:W442–W448.
   PubMed Web of Science ®Google Scholar
• Pharmit, Computational and Systems Biology, University of Pittsburgh, 2016.
   Google Scholar
• Pirhadi S, Shiri F, Ghasemi JB. Methods and applications of structure based pharmacophores in drug discovery. Curr Top Med Chem. 2013;13:1036–1047.
   PubMed Web of Science ®Google Scholar
• Koes DR, Baumgartner MP, Camacho CJ. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model. 2013;53:1893.
   PubMed Web of Science ®Google Scholar
• smina, Computational and Systems Biology, University of Pittsburgh, 2017.
   Google Scholar
• Nazarshodeh E, Shiri F, Ghasemi JB. 3D-QSAR and virtual screening studies in identification of new Rho kinase inhibitors with different scaffolds. J Iranian Chem Soc. 2015;12:1945–1959.
   Web of Science ®Google Scholar
• Wang N-N, Dong J, Deng Y-H, et al. ADME properties evaluation in drug discovery: prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting. J Chem Inf Model. 2016;56:763–773.
   PubMed Web of Science ®Google Scholar
• Gunaratna C. Drug metabolism & pharmacokinetics in drug discovery: a primer for bioanalytical chemists, part I. Curr Separations 2000;19:17–24.
   Google Scholar
• Reichel A, Begley DJ. Potential of immobilized artificial membranes for predicting drug penetration across the blood − brain barrier. Pharm Res. 1998;15:1270–1274.
   PubMed Web of Science ®Google Scholar
• Lombardo F, Gifford E, Shalaeva MY. In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem. 2003;3:861–875.
   PubMed Web of Science ®Google Scholar
• Testa B, Cruciani G. Structure–metabolism relations, and the challenge of predicting biotransformation. In: Pharmacokinetic optimization in drug research: biological, physicochemical and computational chemistry. Zurich (Switzerland): Wiley-VHCA; 2001. p. 65–84.
   Google Scholar
• datawarrior, Openmolecules, 2014.
   Google Scholar
• OSIRIS, Organic Chemistry Portal, 2016.
   Google Scholar
• Van De Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2:192–204.
   PubMed Web of Science ®Google Scholar
• Palm K, Stenberg P, Luthman K, et al. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm Res. 1997;14:568–571.
   PubMed Web of Science ®Google Scholar