Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions.

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound's IC50 value was between 10 µM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.

The searching for agents for Alzheimer's disease treatment via the system of self-consistent models.

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.

Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1).

Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.

Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2.

CORAL software has been used to build quantitative structure-activity relationships (QSARs) for the prediction of binding affinities (pEC50, i.e., minus decimal logarithm of the 50% effective concentration) of 35 potent inhibitors towards the voltage-gated potassium channel subunit Kv7.2. The pEC50 has been modelled using eight random splits, with the following representations of the molecular structure: (i) hydrogen-suppressed graph (HSG); (ii) simplified molecular input line entry system (SMILES); (iii) graph atomic orbitals (GAOs) and (iv) hybrid representation, which is HSG together with SMILES. These models have been examined using three methods, the classic scheme, balance correlation, and balance correlation with ideal slope. The QSAR model based on single optimal descriptors using SMILES provided the best accuracy for the prediction of the pEC50. The robustness of these models has been checked using parameters such as rm(2), r(*)m(2), [Formula: see text], and using a randomization technique. The best QSAR model based on single optimal descriptors has been applied to study the in vitro structure-activity relationships of pyrazolo[1,5-a]pyrimidin-7(4H)-one derivatives as Kv7.2 modulators. The pEC50 is found to be significantly increased by the incorporation of -OH, -NO2 or -Br groups in place of one -F, whereas -NH2 has a negative effect on the pEC50 values.