No abstract

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.

No abstract

a b s t r a c tIn this study, novel (E)-3-(5-substituted-1H-indol-3-yl)-1- (5,5,8,8-tetramethyl-5,6,7,8-tetrahydronaphthalen-2-yl)prop-2-en-1-one (5(aee)) derivatives were synthesized and their anticancer effects were determined in vitro. Novel indole retinoid compounds except 5e have anti-proliferative capacity in liver, breast and colon cancer cell lines. This anti-proliferative effect was further analyzed in breast cancer cell line panel by using the most potent compound 5a. It was determined that 5a can inhibit proliferation at very low IC 50 concentrations in all of the breast cancer cell lines. Here, we present some evidence on apoptotic termination of cancer cell proliferation which may be primarily driven by the inhibition of RXRa and, to a lesser extent, RXRg.

Background: Poly (ADP-ribosyl) polymerase-1 (PARP-1) inhibitors are compounds that are used to treat cancers, which are defective in DNA-repair and DNA Damage-Response (DDR) pathways. Objective: In this study, a series of potential PARP-1 inhibitor substituted (piperazine-1-carbonyl)phenyl)-1H- benzo[d]imidazole4-carboxamide compounds were synthesised and tested for their PARP-1 inhibitory and anti-cancer activities. Methods: Compounds were tested by cell free colorimetric PARP-1 activity and MTT assay in MDA-MB-231, MDA-MB-436, MDA-MB-468 breast cancer and L929 fibroblast cell lines. Results: Our results showed that compound 6a inhibited viability in MDA-MB-231 and MDA-MB-468 cells whereas 8a inhibited viability in MDA-MB-468 cells. Compound 6b significantly inhibited cell viability in tested cancer cells. However, 6b exhibited toxicity in L929 cells whereas 6a and 8a were found to be non-toxic for L929 cells. Compounds 6a, 6b and 8a exhibited significant inhibition of PARP-1 activity. Conclusion: These three compounds exhibited PARP-1 inhibitory activities and anticancer effects on breast cancer cells and further research will enlighten the underlying mechanisms of their effects.