QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads.

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

Quantitative relationships between structure and lipophilicity of naturally occurring polyphenols.

The lipophilicity of polyphenols inherent in food, beverages, and medicinal plants was modelled by using 3D descriptors derived from optimized 3D molecular structures in combination with 2D descriptors. The training sets were generated by manual selection or by cluster formation, and statistically robust predictive models were obtained in both cases. The most relevant structural features for the lipophilicity of polyphenols are depicted by the statistically most significant variables: the number of donor atoms for the H bonds is unfavorable for lipophilicity, and the enhanced number of ring secondary C atom (sp3) also decreases lipophilicity, while the increased atomic polarizability implies higher lipophilicity of polyphenols. The study also revealed the importance of a three-dimensional distribution of atomic electronegativity for the lipophilicity of molecules.