Next Article in Journal
Hemodynamic Forces Regulate Cardiac Regeneration-Responsive Enhancer Activity during Ventricle Regeneration
Next Article in Special Issue
Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs
Previous Article in Journal
Outer Membrane Vesicle Production by Helicobacter pylori Represents an Approach for the Delivery of Virulence Factors CagA, VacA and UreA into Human Gastric Adenocarcinoma (AGS) Cells
Previous Article in Special Issue
An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming
Article

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

[email protected]/Faculty of Sciences, University of Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Hanoch Senderowitz
Int. J. Mol. Sci. 2021, 22(8), 3944; https://doi.org/10.3390/ijms22083944
Received: 14 February 2021 / Revised: 7 April 2021 / Accepted: 8 April 2021 / Published: 11 April 2021
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)
AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors. View Full-Text
Keywords: AKT inhibitors; multi-target QSAR models; pharmacophore-based mapping; molecular docking; molecular dynamics simulations AKT inhibitors; multi-target QSAR models; pharmacophore-based mapping; molecular docking; molecular dynamics simulations
Show Figures

Figure 1

MDPI and ACS Style

Halder, A.K.; Cordeiro, M.N.D.S. AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery. Int. J. Mol. Sci. 2021, 22, 3944. https://doi.org/10.3390/ijms22083944

AMA Style

Halder AK, Cordeiro MNDS. AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery. International Journal of Molecular Sciences. 2021; 22(8):3944. https://doi.org/10.3390/ijms22083944

Chicago/Turabian Style

Halder, Amit K., and M. N.D.S. Cordeiro. 2021. "AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery" International Journal of Molecular Sciences 22, no. 8: 3944. https://doi.org/10.3390/ijms22083944

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop