Quantitative structure–activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks

Abstract

Background After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds. Methods In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg–Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity. Results The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R2), mean square error (MSE). For instance R2train = 0.947, R2val = 0.959, R2test = 0.920 shows the potential of the suggested model for the prediction of 3D7 activity. Different statistical approaches such as and applicability domain (AD) and y-scrambling was also showed the validity of models. Conclusion QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial–error runs during synthesis.

Design of N-11-Azaartemisinins Potentially Active against <i>Plasmodium falciparum</i> by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

“…About the investigated N-11-azaartemisinis) also exhibit the endoperoxide linkage necessary for antimalarial activity. Figure 3 shows the MEP maps for the N-11-azaartemisinins of the training set obtained by the inclusion of substituents in the N atom of the lactam function (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). As one can see in this figure, the MEP maps are similar to artemisinin and 11-azaartemisinin in the 1, 2, 4 trioxane ring region, with the electron density of some molecules more concentrated in this region, indicating greater biological activity.…”

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

“…Interestingly, another study demonstrated a 2D-QSAR model of 3133 compounds using 929 descriptors in which the study showed abysmal 14.2% accuracy [ 96 ]. A similar study applied Artificial Neural Networks with Levenberg–Marquardt algorithm (non-linear approach) on the anti-malarial activity of a set of 33 imidazolopiperazine compounds against 3D7 and W2 strains [ 97 ]. Results showed the potential of the suggested model for the prediction of 3D7 activity and more acceptable than W2 strain with R 2 train = 0.947, R 2 val = 0.959, R 2 test = 0.920.…”

Digital Pharmaceutical Sciences

“…- For ROCK datasets: 6 molecular descriptors (related 2D descriptors such as ring count) Prediction of the biological activity of various phenols and Rho kinase (ROCK) inhibitors. ( 57 ) ANN Linear partial least squares (linear statistical method) Supervised 36 4 molecular descriptors: - minimum bond dissociation enthalpy - electron transfer enthalpy - proton affinity - hydration energy Prediction of antioxidant activity of flavonoids ( 58 ) RF ANN Supervised 91 166 molecular descriptors including: - structure - topology - molecular connectivity index - geometric descriptors Prediction of the carcinogenicity of polycyclic aromatic hydrocarbons ( 59 ) ANN Supervised 33 6 molecular descriptors (related 2D and 3D descriptors) Prediction of anti-malarial activity of imidazolopiperazine compounds ( 60 ) ANN SVM Supervised 639 341 molecular descriptors related to: - simple constitutional - topological indices - electrotopological state indices - charge-based - hydrogen-bonding descriptors Prediction of nephrotoxicity of traditional Chinese medicines ingredients ( 61 ) * The top-ranked machine learning methods in each of these studies demonstrated better predictive ability than the other machine learning methods tested. ANN artificial neural network, SVM support vector machine, DT decision tree, RF random forest, KNN K-nearest neighbor, RBFNN radial basis function neural network …”