Design, Bio-evaluation and Molecular Dynamics Simulation of Novel GSK-3β Inhibitors

Discovery, Structure-Based Modification, In Vitro, In Vivo, and In Silico Exploration of m-Sulfamoyl Benzoamide Derivatives as Selective Butyrylcholinesterase Inhibitors for Treating Alzheimer's Disease

For the potential therapy of Alzheimer's disease (AD), butyrylcholinesterase (BChE) has gradually gained worldwide interest in the progression of AD. This study used a pharmacophore-based virtual screening (VS) approach to identify Z32439948 as a new BChE inhibitor. Aiding by molecular docking and molecular dynamics, essential binding information was disclosed. Specifically, a subpocket was found and structure-guided design of a series of novel compounds was conducted. Derivatives were evaluated in vitro for cholinesterase inhibition and physicochemical properties (BBB, log P, and solubility). The investigation involved docking, molecular dynamics, enzyme kinetics, and surface plasmon resonance as well. The study highlighted compounds 27a (hBChE IC50 = 0.078 ± 0.03 μM) and (R)-37a (hBChE IC50 = 0.005 ± 0.001 μM) as the top-ranked BChE inhibitors. These compounds showed anti-inflammatory activity and no apparent cytotoxicity against the human neuroblastoma (SH-SY5Y) and mouse microglia (BV2) cell lines. The most active compounds exhibited the ability to improve cognition in both scopolamine- and Aβ1-42 peptide-induced cognitive deficit models. They can be promising lead compounds with potential implications for treating the late stage of AD.

LCP: Simple Representation of Docking Poses for Machine Learning: A Case Study on Xanthine Oxidase Inhibitors

In this paper, we propose a simple descriptor called the ligand coordinate profile (LCP) for describing docking poses. The LCP descriptor is generated from the coordinates of the polar hydrogen and heavy atoms of the docked ligand. We hypothesize that the prediction of binding poses can be enhanced through the combination of machine learning methods with the LCP descriptor. Two docking programs were used to predict ligand docking against xanthine oxidase. Four machine learning methods-k-nearest neighbors, random forest, support vector machine, and LightGBM-were used to determine whether machine learning-based models could be used to accurately identify the correct binding poses. Regardless of the machine learning method employed, the LCP descriptor demonstrated improved performance compared to the existing descriptor. The results of the leave-one-pdb-out approach revealed that the influence of the pose descriptor was also significant, as demonstrated through cross-validation. When evaluated using top-N metrics, the machine learning models were generally more effective than the docking programs. In addition, the LCP-based models outperformed those based on the existing descriptor. The results obtained in this study suggest that our proposed binding pose descriptor is effective for improving the docking accuracy of xanthine oxidase inhibitors.