Research Article
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Year 2020, Volume: 4 Issue: 2, 49 - 58, 15.12.2020
https://doi.org/10.33435/tcandtc.624156

Abstract

References

  • [1] I.E. Emmanuel,U. Adamu, E. A. Stephen, Quantitative structure and activity relationship modelling study of anti-HIV-1 RT inhibitors: Genetic function approximation and density functional theory method. Journal computational methods in molecular Designs, 5 (2015) 61-76.
  • [2] Di Santo, Inhibiting the HIV integration process: past, present and future. J med chem 51 (2014) 539-566.
  • [3] D.S. Ruelas , W.C. Greene , An integrated overview of HIV-1 latency. Cell, Elsevier Inc. 155 (2013) 519-529.
  • [4] S. Wang, P. Hortz, M. Schechter, L. Rong, Modelling the slow CD4 + T cell Decline in HIV-infected individuals, PLOS Computational Biology 11 (2015).
  • [5] A.E. Shola, M.O. Idris, S. Tukur, A.U Saviour, G.A. Shallangwa, A. Uzairu, Theoretical modelling for investigating some active compounds as potent inhibitors against lung cancer. Journal of Engineering and exact science 5(2019) 0125-0136.
  • [6] F. Soualmia, S. Belaidi, N. Tchouar, T. Lanez, Review of computational studies applied in new macrolide antibiotics, Journal of fundamental and applied sciences, 12 (2020) 392-415.
  • [7] J.S. Jaworska, M. Comber, C.Auer , C.J.Van Leeuwen, Summary of a workshop on regulatory acceptance of QSARs for human health and environmental endpoints, Environmental Health Perspectives, 111 (2003) 1358–1360.
  • [8] G. Liu, R. Luo, X. ZHANG, Y. ZHOU, J. LI, Y. ZHENG, H. Liu, Synthesis and Evaluation of Anti-HIV Activities of Novel 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives, 4 (2014) 573-580.
  • [9] Y. He, C. Y. Liew, N. Sharma, S. K. Woo, Y. T. Chau, C. W. Yap, PaDEL-Descriptor: Open source software for PD-PK-T prediction, Journal of computational chemistry, 34 (2013) 604-610.
  • [10] A.E.Shola, S. Uba, A. Uzairu, In Silico Study for investigating and predicting the activities of 1, 2, 4-Tirazole Derivatives as potent Anti-Tubercular agents. The Journal of Engineering and Exact Science 4 (2018) 0246-0254.
  • [11] Alexander Tropsha. Best practices for QSAR model Development, validation and exploitation, Molecular Informatics. Inf. 29 (2010) 476-488.
  • [12] A. Golbraikh; A Tropsha, Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection, J Comput Aided Mol Des., 20 (2002) 269–276.
  • [13] A. Racz, D. Bajusz, K. Heberger, Intercorrelation limits in molecular descriptors preselection for QSAR/QSPR, Molecular informatics 38 (2019) 1-6.
  • [14] K. Roy, J.T. Leonard, On selection of training and test sets for the development of predictive QSAR models. QSAR and Combinatorial Science 25 (2006) 235-251.
  • [15] M. Patel, N. Malle Shappa, S. Poonam, J. Varun, B. Sumit, L. Sandeep, A. Vikrant, D. Saurabh, B. Varun, QSAR studies as strategic approach in drug discovery. Med Chem 23 (2014) 4991-5007.
  • [16] J.H. Holland, Adaptation in natural and artificial system, University of Michigan Press. (1975).
  • [17] V. Ravinchandran, R. Harish, J. Abhishek, S. Shalini, P.V. Christapher, K.A. Ram, Validation of QSAR models-strategies and importance, International Journal of Drug Design and Discovery, 2 (2011) 511-519.
  • [18] A.Tropsha, P. Grammatica, V.K. Gombar, The importance of being Earnest: Validation is the Absolute essential for successful Application and interpretation of QSAR models, QSAR and Combinatorial Science 22 (2003) 69-77.
  • [19] K. Roy, Some aspects of validation of predictive quantitative structure-activity relationship models, Expert Opinion On Drug Discovery 2 (2007) 1567-1577.
  • [20] A.S. Ugochukwu, G. A. Shallangwa, A. Uzairu, Quantitative Structure and Activity Relationship of 3a, 6a – Dihydro-1H-pyrrolo [3,4-c]pyrazole-4,6-dione Derivatives, Turkish Computational and Theoretical Chemistry. 4 (2020) 32-39.

In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents

Year 2020, Volume: 4 Issue: 2, 49 - 58, 15.12.2020
https://doi.org/10.33435/tcandtc.624156

Abstract

In this study a QSAR was carried out on a data set of 7-Hydroxy-1,3- dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives to investigate their activities on HIV-1. Genetic Function Algorithm(GFA) and Multi Linear Regression Analysis (MLRA) were used to select the optimum descriptors and to generate the correlation QSAR model that relate their activities against HIV with the molecular structures of the derivatives. After the internal validation, the model was found to have a squared correlation coefficient (R2) of 0.9334, adjusted squared correlation coefficient (R2adj) of 0.9134 and leave one out cross validated coefficient (LOO- Q2cv) value of 0.8604. The external validation (R2pred) set used for confirming the predictive power of the model was 0.8935. Y randomization value of 0.6463 was used to confirm the robustness of the model. The robustness and stability of the model obtained by validation of the test set also confirmed that the model can be used to design and synthesize other 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives with improved Anti- HIV activities.

References

  • [1] I.E. Emmanuel,U. Adamu, E. A. Stephen, Quantitative structure and activity relationship modelling study of anti-HIV-1 RT inhibitors: Genetic function approximation and density functional theory method. Journal computational methods in molecular Designs, 5 (2015) 61-76.
  • [2] Di Santo, Inhibiting the HIV integration process: past, present and future. J med chem 51 (2014) 539-566.
  • [3] D.S. Ruelas , W.C. Greene , An integrated overview of HIV-1 latency. Cell, Elsevier Inc. 155 (2013) 519-529.
  • [4] S. Wang, P. Hortz, M. Schechter, L. Rong, Modelling the slow CD4 + T cell Decline in HIV-infected individuals, PLOS Computational Biology 11 (2015).
  • [5] A.E. Shola, M.O. Idris, S. Tukur, A.U Saviour, G.A. Shallangwa, A. Uzairu, Theoretical modelling for investigating some active compounds as potent inhibitors against lung cancer. Journal of Engineering and exact science 5(2019) 0125-0136.
  • [6] F. Soualmia, S. Belaidi, N. Tchouar, T. Lanez, Review of computational studies applied in new macrolide antibiotics, Journal of fundamental and applied sciences, 12 (2020) 392-415.
  • [7] J.S. Jaworska, M. Comber, C.Auer , C.J.Van Leeuwen, Summary of a workshop on regulatory acceptance of QSARs for human health and environmental endpoints, Environmental Health Perspectives, 111 (2003) 1358–1360.
  • [8] G. Liu, R. Luo, X. ZHANG, Y. ZHOU, J. LI, Y. ZHENG, H. Liu, Synthesis and Evaluation of Anti-HIV Activities of Novel 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives, 4 (2014) 573-580.
  • [9] Y. He, C. Y. Liew, N. Sharma, S. K. Woo, Y. T. Chau, C. W. Yap, PaDEL-Descriptor: Open source software for PD-PK-T prediction, Journal of computational chemistry, 34 (2013) 604-610.
  • [10] A.E.Shola, S. Uba, A. Uzairu, In Silico Study for investigating and predicting the activities of 1, 2, 4-Tirazole Derivatives as potent Anti-Tubercular agents. The Journal of Engineering and Exact Science 4 (2018) 0246-0254.
  • [11] Alexander Tropsha. Best practices for QSAR model Development, validation and exploitation, Molecular Informatics. Inf. 29 (2010) 476-488.
  • [12] A. Golbraikh; A Tropsha, Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection, J Comput Aided Mol Des., 20 (2002) 269–276.
  • [13] A. Racz, D. Bajusz, K. Heberger, Intercorrelation limits in molecular descriptors preselection for QSAR/QSPR, Molecular informatics 38 (2019) 1-6.
  • [14] K. Roy, J.T. Leonard, On selection of training and test sets for the development of predictive QSAR models. QSAR and Combinatorial Science 25 (2006) 235-251.
  • [15] M. Patel, N. Malle Shappa, S. Poonam, J. Varun, B. Sumit, L. Sandeep, A. Vikrant, D. Saurabh, B. Varun, QSAR studies as strategic approach in drug discovery. Med Chem 23 (2014) 4991-5007.
  • [16] J.H. Holland, Adaptation in natural and artificial system, University of Michigan Press. (1975).
  • [17] V. Ravinchandran, R. Harish, J. Abhishek, S. Shalini, P.V. Christapher, K.A. Ram, Validation of QSAR models-strategies and importance, International Journal of Drug Design and Discovery, 2 (2011) 511-519.
  • [18] A.Tropsha, P. Grammatica, V.K. Gombar, The importance of being Earnest: Validation is the Absolute essential for successful Application and interpretation of QSAR models, QSAR and Combinatorial Science 22 (2003) 69-77.
  • [19] K. Roy, Some aspects of validation of predictive quantitative structure-activity relationship models, Expert Opinion On Drug Discovery 2 (2007) 1567-1577.
  • [20] A.S. Ugochukwu, G. A. Shallangwa, A. Uzairu, Quantitative Structure and Activity Relationship of 3a, 6a – Dihydro-1H-pyrrolo [3,4-c]pyrazole-4,6-dione Derivatives, Turkish Computational and Theoretical Chemistry. 4 (2020) 32-39.
There are 20 citations in total.

Details

Primary Language English
Subjects Chemical Engineering
Journal Section Research Article
Authors

Ahanonu Saviour This is me 0000-0001-5023-7207

Gideon Adamu Shallangwa This is me

Adamu Uzairu This is me

Publication Date December 15, 2020
Submission Date September 25, 2019
Published in Issue Year 2020 Volume: 4 Issue: 2

Cite

APA Saviour, A., Shallangwa, G. A., & Uzairu, A. (2020). In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents. Turkish Computational and Theoretical Chemistry, 4(2), 49-58. https://doi.org/10.33435/tcandtc.624156
AMA Saviour A, Shallangwa GA, Uzairu A. In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents. Turkish Comp Theo Chem (TC&TC). December 2020;4(2):49-58. doi:10.33435/tcandtc.624156
Chicago Saviour, Ahanonu, Gideon Adamu Shallangwa, and Adamu Uzairu. “In Silico Study for Investigating and Predicting the Activities of 7-Hydroxy-1,3-Dioxo-2,3-Dihydro-1H-pyrrolo[3,4-c]pyridine-4-Carboxylate Derivatives As Potent Anti-HIV Agents”. Turkish Computational and Theoretical Chemistry 4, no. 2 (December 2020): 49-58. https://doi.org/10.33435/tcandtc.624156.
EndNote Saviour A, Shallangwa GA, Uzairu A (December 1, 2020) In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents. Turkish Computational and Theoretical Chemistry 4 2 49–58.
IEEE A. Saviour, G. A. Shallangwa, and A. Uzairu, “In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents”, Turkish Comp Theo Chem (TC&TC), vol. 4, no. 2, pp. 49–58, 2020, doi: 10.33435/tcandtc.624156.
ISNAD Saviour, Ahanonu et al. “In Silico Study for Investigating and Predicting the Activities of 7-Hydroxy-1,3-Dioxo-2,3-Dihydro-1H-pyrrolo[3,4-c]pyridine-4-Carboxylate Derivatives As Potent Anti-HIV Agents”. Turkish Computational and Theoretical Chemistry 4/2 (December 2020), 49-58. https://doi.org/10.33435/tcandtc.624156.
JAMA Saviour A, Shallangwa GA, Uzairu A. In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents. Turkish Comp Theo Chem (TC&TC). 2020;4:49–58.
MLA Saviour, Ahanonu et al. “In Silico Study for Investigating and Predicting the Activities of 7-Hydroxy-1,3-Dioxo-2,3-Dihydro-1H-pyrrolo[3,4-c]pyridine-4-Carboxylate Derivatives As Potent Anti-HIV Agents”. Turkish Computational and Theoretical Chemistry, vol. 4, no. 2, 2020, pp. 49-58, doi:10.33435/tcandtc.624156.
Vancouver Saviour A, Shallangwa GA, Uzairu A. In Silico study for investigating and predicting the activities of 7-Hydroxy-1,3-dioxo-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-4-carboxylate Derivatives as Potent Anti-HIV Agents. Turkish Comp Theo Chem (TC&TC). 2020;4(2):49-58.

Journal Full Title: Turkish Computational and Theoretical Chemistry


Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)