Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7

Georges Stéphane Dembélé; Mamadou Guy-Richard Koné; Bafétigué Ouattara; Fandia Konate; Doh Soro; Nahossé Ziao

doi:doi:10.11648/j.ajpc.20221101.11

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Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7

Georges Stéphane Dembélé, Mamadou Guy-Richard Koné, Bafétigué Ouattara, Fandia Konate, Doh Soro, Nahossé Ziao

Published in American Journal of Physical Chemistry (Volume 11, Issue 1)

Received: 21 December 2021 Accepted: 7 January 2022 Published: 12 January 2022

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Abstract

This Quantitative Structure-Activity Relationship (QSAR) study was conducted using a series of twenty (20) chalcone derivatives with inhibitory activities against Plasmodium falciparum 3D7. The molecules were optimized at the B3LYP/LanL2DZ computational level, to obtain the molecular descriptors. This work was performed using the Linear Multiple Regression (LMR) method, the NonLinear Regression (NLMR) and the Artificial Neural Network (ANN) method. These tools allowed us to obtain three (3) quantitative models from the quantum descriptors that are, the overall softness (S), the bond lengths l(c=o) and l(c=c), and the polarizability (α). These models have good statistical performance. Among them, the ANN has a significantly better predictive ability R² =0.997; RMCE = 0.035; F= 3571.499. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the applicability domain of this model determined from the levers shows that a prediction of the pIC50 of new chalcone derivatives is acceptable when its lever value is lower than 1.07. For the ANN method, the Ch19 molecule is certainly outside the applicability domain, but it is not an influential point for the model, because this derivative belongs to the validation set, and therefore was not used in the model development. The behavior of this molecule could be explained by its structural diversity.

Published in	American Journal of Physical Chemistry (Volume 11, Issue 1)
DOI	10.11648/j.ajpc.20221101.11
Page(s)	1-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Plasmodium Falciparum 3D7, Chalcone Derivatives, RQSA, RNA, Applicability Domain

References

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Georges Stéphane Dembélé, Mamadou Guy-Richard Koné, Bafétigué Ouattara, Fandia Konate, Doh Soro, et al. (2022). Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. American Journal of Physical Chemistry, 11(1), 1-13. https://doi.org/10.11648/j.ajpc.20221101.11

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Georges Stéphane Dembélé; Mamadou Guy-Richard Koné; Bafétigué Ouattara; Fandia Konate; Doh Soro, et al. Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. Am. J. Phys. Chem. 2022, 11(1), 1-13. doi: 10.11648/j.ajpc.20221101.11

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AMA Style

Georges Stéphane Dembélé, Mamadou Guy-Richard Koné, Bafétigué Ouattara, Fandia Konate, Doh Soro, et al. Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. Am J Phys Chem. 2022;11(1):1-13. doi: 10.11648/j.ajpc.20221101.11

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@article{10.11648/j.ajpc.20221101.11,
  author = {Georges Stéphane Dembélé and Mamadou Guy-Richard Koné and Bafétigué Ouattara and Fandia Konate and Doh Soro and Nahossé Ziao},
  title = {Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7},
  journal = {American Journal of Physical Chemistry},
  volume = {11},
  number = {1},
  pages = {1-13},
  doi = {10.11648/j.ajpc.20221101.11},
  url = {https://doi.org/10.11648/j.ajpc.20221101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpc.20221101.11},
  abstract = {This Quantitative Structure-Activity Relationship (QSAR) study was conducted using a series of twenty (20) chalcone derivatives with inhibitory activities against Plasmodium falciparum 3D7. The molecules were optimized at the B3LYP/LanL2DZ computational level, to obtain the molecular descriptors. This work was performed using the Linear Multiple Regression (LMR) method, the NonLinear Regression (NLMR) and the Artificial Neural Network (ANN) method. These tools allowed us to obtain three (3) quantitative models from the quantum descriptors that are, the overall softness (S), the bond lengths l(c=o) and l(c=c), and the polarizability (α). These models have good statistical performance. Among them, the ANN has a significantly better predictive ability R2 =0.997; RMCE = 0.035; F= 3571.499. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the applicability domain of this model determined from the levers shows that a prediction of the pIC50 of new chalcone derivatives is acceptable when its lever value is lower than 1.07. For the ANN method, the Ch19 molecule is certainly outside the applicability domain, but it is not an influential point for the model, because this derivative belongs to the validation set, and therefore was not used in the model development. The behavior of this molecule could be explained by its structural diversity.},
 year = {2022}
}

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TY - JOUR
T1 - Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7
AU - Georges Stéphane Dembélé
AU - Mamadou Guy-Richard Koné
AU - Bafétigué Ouattara
AU - Fandia Konate
AU - Doh Soro
AU - Nahossé Ziao
Y1 - 2022/01/12
PY - 2022
N1 - https://doi.org/10.11648/j.ajpc.20221101.11
DO - 10.11648/j.ajpc.20221101.11
T2 - American Journal of Physical Chemistry
JF - American Journal of Physical Chemistry
JO - American Journal of Physical Chemistry
SP - 1
EP - 13
PB - Science Publishing Group
SN - 2327-2449
UR - https://doi.org/10.11648/j.ajpc.20221101.11
AB - This Quantitative Structure-Activity Relationship (QSAR) study was conducted using a series of twenty (20) chalcone derivatives with inhibitory activities against Plasmodium falciparum 3D7. The molecules were optimized at the B3LYP/LanL2DZ computational level, to obtain the molecular descriptors. This work was performed using the Linear Multiple Regression (LMR) method, the NonLinear Regression (NLMR) and the Artificial Neural Network (ANN) method. These tools allowed us to obtain three (3) quantitative models from the quantum descriptors that are, the overall softness (S), the bond lengths l(c=o) and l(c=c), and the polarizability (α). These models have good statistical performance. Among them, the ANN has a significantly better predictive ability R2 =0.997; RMCE = 0.035; F= 3571.499. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the applicability domain of this model determined from the levers shows that a prediction of the pIC50 of new chalcone derivatives is acceptable when its lever value is lower than 1.07. For the ANN method, the Ch19 molecule is certainly outside the applicability domain, but it is not an influential point for the model, because this derivative belongs to the validation set, and therefore was not used in the model development. The behavior of this molecule could be explained by its structural diversity.
VL - 11
IS - 1
ER -

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Author Information

Georges Stéphane Dembélé

Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire
Mamadou Guy-Richard Koné

Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire
Bafétigué Ouattara

Laboratoire de Physique Fondamentale et Appliquée, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire
Fandia Konate

Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire
Doh Soro

Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire
Nahossé Ziao

Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université NANGUI ABROGOUA, Abidjan, C?te-d’Ivoire

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Georges Stéphane Dembélé, Mamadou Guy-Richard Koné, Bafétigué Ouattara, Fandia Konate, Doh Soro, et al. (2022). Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. American Journal of Physical Chemistry, 11(1), 1-13. https://doi.org/10.11648/j.ajpc.20221101.11

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Georges Stéphane Dembélé; Mamadou Guy-Richard Koné; Bafétigué Ouattara; Fandia Konate; Doh Soro, et al. Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. Am. J. Phys. Chem. 2022, 11(1), 1-13. doi: 10.11648/j.ajpc.20221101.11

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AMA Style

Georges Stéphane Dembélé, Mamadou Guy-Richard Koné, Bafétigué Ouattara, Fandia Konate, Doh Soro, et al. Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7. Am J Phys Chem. 2022;11(1):1-13. doi: 10.11648/j.ajpc.20221101.11

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@article{10.11648/j.ajpc.20221101.11,
  author = {Georges Stéphane Dembélé and Mamadou Guy-Richard Koné and Bafétigué Ouattara and Fandia Konate and Doh Soro and Nahossé Ziao},
  title = {Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7},
  journal = {American Journal of Physical Chemistry},
  volume = {11},
  number = {1},
  pages = {1-13},
  doi = {10.11648/j.ajpc.20221101.11},
  url = {https://doi.org/10.11648/j.ajpc.20221101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpc.20221101.11},
  abstract = {This Quantitative Structure-Activity Relationship (QSAR) study was conducted using a series of twenty (20) chalcone derivatives with inhibitory activities against Plasmodium falciparum 3D7. The molecules were optimized at the B3LYP/LanL2DZ computational level, to obtain the molecular descriptors. This work was performed using the Linear Multiple Regression (LMR) method, the NonLinear Regression (NLMR) and the Artificial Neural Network (ANN) method. These tools allowed us to obtain three (3) quantitative models from the quantum descriptors that are, the overall softness (S), the bond lengths l(c=o) and l(c=c), and the polarizability (α). These models have good statistical performance. Among them, the ANN has a significantly better predictive ability R2 =0.997; RMCE = 0.035; F= 3571.499. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the applicability domain of this model determined from the levers shows that a prediction of the pIC50 of new chalcone derivatives is acceptable when its lever value is lower than 1.07. For the ANN method, the Ch19 molecule is certainly outside the applicability domain, but it is not an influential point for the model, because this derivative belongs to the validation set, and therefore was not used in the model development. The behavior of this molecule could be explained by its structural diversity.},
 year = {2022}
}

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