Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking

Abstract

Background The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques. Results The developed QSAR model with predicted (R2pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R2 for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of −6.327 and −7.232 kcalmol−1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (−6.0 kcalmol−1). This could be attributed to the presence of double bonds and the α-ester groups. Conclusion The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.

Isolation, Cytotoxicity Evaluation, Docking, ADMET and Drug Likeness Studies of Secondary Metabolites from the Stem Bark of Anthocephalus cadamba (Roxb.)

“…Docking Studies:The docking studies of all the compounds performed to understand the mechanism of action against pancreatic cancer cell line (Mia-Pa-Ca-2) by targeting the epidermal growth factor receptor (EGFR) (PDB ID:3POZ). [31] Monomeric structure model of human EGFR is having a chain of 327 amino acids. Three-dimensional structure model of human EGFR macromolecular complex was resolved by X-ray diffraction at a resolution of 1.50 Å.…”

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

“…Unlike the backpropagation (BP) technique in conventional neural networks, with its attendant shortfalls, the ELM has the ability to avoid the need for numerous iterations and local minima problems [41]. It has, therefore, gained widespread usage in classification and regression tasks owing to its superior generalization capabilities, fast learning speed and unresponsiveness to parameters pre-defined by the user [31,[42][43][44][45][46]. The ELM algorithm works by randomly generating the input weights and biases of the hidden layer.…”

Extreme Learning Machine Approach to Modeling the Superconducting Critical Temperature of Doped MgB2 Superconductor

“…Another interesting merit of a computational-intelligence-based extreme learning machine is the random generation of the hidden layer threshold, as well as the input layer weights, in contradistinction to the classical, feed-forward neural networks that mostly employ the ladder descent method for their training process. These features significantly promote and widen the application of the ELM algorithm in many fields of applications [20][21][22][23][24]. The influence of noise insertion on machine learning techniques has been treated elsewhere [25].…”