A series of substituted quinolines was screened for their antiproliferative, cytotoxic, antibacterial activities, DNA/protein binding affinity, and anticholinergic properties by using the 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide cell proliferation, lactate dehydrogenase cytotoxicity, and microdilution assays, the Wolfe–Shimmer equality method, the Ellman method, and the esterase assay, respectively. The results of the cytotoxic and anticancer activities of the compounds displayed that 6‐bromotetrahydroquinoline (2), 6,8‐dibromotetrahydroquinoline (3), 8‐bromo‐6‐cyanoquinoline (10), 5‐bromo‐6,8‐dimethoxyquinoline (12), the novel N‐nitrated 6,8‐dimethoxyquinoline (13), and 5,7‐dibromo‐8‐hydroxyquinoline (17) showed a significant antiproliferative potency against the A549, HeLa, HT29, Hep3B, and MCF7 cancer cell lines (IC50 = 2–50 μg/ml) and low cytotoxicity (∼7–35%) as the controls, 5‐fluorouracil and cisplatin. The compound–DNA linkages are hyperchromic or hypochromic, causing variations in their spectra. This situation shows that they can be bound to DNA with the groove‐binding mode, with Kb value in the range of 2.0 × 103–2.2 × 105 M–1. Studies on human Gram(+) and Gram(−) pathogenic bacteria showed that the substituted quinolines exhibited selective antimicrobial activities with MIC values of 62.50–250 μg/ml. All tested quinoline derivatives were found to be effective inhibitors of acetylcholinesterase (AChE) and the human carbonic anhydrase I and II isoforms (hCA I and II), with Ki values of 46.04–956.82 nM for hCA I, 54.95–976.93 nM for hCA II, and 5.51–155.22 nM for AChE. As a result, the preliminary data showed that substituted quinolines displayed effective pharmacological features. Molecular docking studies were performed to investigate the binding modes and interaction energies for compounds 2–17 with AChE (PDB ID: 4EY6), hCA I (PDB ID: 1BMZ), and hCA II (PDB ID: 2ABE).

Analyses of known protein–ligand interactions play an important role in designing novel and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have proven useful in the design of novel drugs, which utilize intelligent techniques to predict the outcome of unknown protein–ligand interactions by learning from the physical and geometrical properties of known protein–ligand interactions. The aim of this study is to work through a specific example of a novel computational method, namely compressed images for affinity prediction (CIFAP), in which binding affinities for structurally related ligands in complexes with human checkpoint kinase 1 (CHK1) are predicted. The CIFAP algorithm presented here relates published pIC 50 values of 57 compounds, derived from a thienopyridine pharmacophore, in complexes with CHK1 to their two‐dimensional (2D) electrostatic potential images compressed in orthogonal dimensions. Patterns obtained from the 2D images are then used as inputs in regression and learning algorithms such as support vector regression (SVR) and adaptive neuro‐fuzzy inference system (ANFIS) methods to validate the experimental pIC 50 values. This study revealed that the 2D image pixels in the vicinity of bound ligand surfaces provide more relevant information to make correlations with the empirical pIC 50 values. As compared with ANFIS, SVR gave rise to the lowest root mean square errors and the greatest correlations, suggesting that SVR could be a plausible choice of machine learning methods in predicting binding affinities by CIFAP. Copyright © 2013 John Wiley & Sons, Ltd.

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