Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations.

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease.

Combined 3D-{QSAR} and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors.

The discovery of antibacterials is considered one of the greatest medical achievements of all time. In this work, a combination of three computational analyzes: 3D-QSAR, molecular docking and ADME evaluation were applied in thienopyrimidine derivatives intended toward gram-positive bacterium Staphylococcus aureus. The validity of 3D-QSAR model was tested with a set of data which is divided into a training and a test set.

and Determination of Some N-ferrocenylmethylaniline Derivatives as Anti-Proliferative Agents Against MCF-7 Human Breast Cancer Cell Lines.

Background: Since the binding of estradiol to its receptor promotes breast cancer cell proliferation (in the ER+ tumours), many molecules targeting this protein have been synthesized to counteract the estradiol action. Ferrocene derivatives have proved their efficiency against hormone-dependent breast cancer cells (MCF-7).

Objective: In this study, we aimed to find new ferrocene derivatives having pharmacochemistry properties as potential drugs against human breast cancer cells.

Multi-combined 3D-QSAR, docking molecular and ADMET prediction of 5-azaindazole derivatives as LRRK2 tyrosine kinase inhibitors.

The enzyme Leucine-rich repeat kinase 2 (LRRK2) has become a target of therapeutic interest in Parkinson research.Athree-dimensional quantitative structure-activity relationships(3D-QSAR) study was performed on twenty six azaindazole derivatives as LRRK2 inhibitorsobtained using rigid body alignment (Distill). CoMFA and CoMSIA model shave achieved high activity-descriptor relationship efficiency of 96% and 93% as shown by the regression-coefficient (R=0.