The rational search for PDE10A inhibitors from Sophora�flavescens roots using pharmacophore‑ and docking‑based virtual screening

Abstract

Phosphodiesterase 10A (PDE10A) has been confirmed to be an important target for the treatment of central nervous system (CNS) disorders. The purpose of the present study was to identify PDE10A inhibitors from herbs used in traditional Chinese medicine. Pharmacophore and molecular docking techniques were used to virtually screen the chemical molecule database of Sophora flavescens, a well‑known Chinese herb that has been used for improving mental health and regulating the CNS. The pharmacophore model generated recognized the common functional groups of known PDE10A inhibitors. In addition, molecular docking was used to calculate the binding affinity of ligand‑PDE10A interactions and to investigate the possible binding pattern. Virtual screening based on the pharmacophore model and molecular docking was performed to identify potential PDE10A inhibitors from S. flavescens. The results demonstrated that nine hits from S. flavescens were potential PDE10A inhibitors, and their biological activity was further validated using literature mining. A total of two compounds were reported to inhibit cyclic adenosine monophosphate phosphodiesterase, and one protected against glutamate‑induced oxidative stress in the CNS. The remaining six compounds require further bioactivity validation. The results of the present study demonstrated that this method was a time‑ and cost‑saving strategy for the identification of bioactive compounds from traditional Chinese medicine.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

“…In this contribution study, we used the UNITY module of SYBYL-X 2.1 to build 3D pharmacophore models for the cervical cancer inhibitors studied. The conformers were generated using the Genetic Algorithm (GA) method and the GALAHAD (Linear Algorithm for Hypermolecular Alignment of Data Sets) platforms [ 62 , 63 ]. For this, the features of the pharmacophore were developed by flexibly superimposing the compounds in the training set to obtain hypermolecular alignments and then validated using the molecules in the test set.…”

Investigative on the Molecular Mechanism of Licorice Flavonoids Anti-Melanoma by Network Pharmacology, 3D/2D-QSAR, Molecular Docking, and Molecular Dynamics Simulation

“…Set the principal and MaxOmitFeat values of compounds higher than 1 μm for IC 50 to 1 (the conformational space should be referred to when modeling, but the modeling result can have a characteristic element that does not match it). Feature mapping was used to identify the characteristic elements of the training set, to study the molecules, including those main characteristic elements, and then set the obtained characteristic elements as the characteristic elements of the pharmacophore effect to be considered by HypoGen, namely hydrogen bond receptor (HBA), hydrogen bond donor (HBD), hydrophobic center (H), cationic group (PI), and aromatic ring center (R) were five items as possible pharmacophore characteristic elements ( Fan et al, 2018 ). The range of each pharmacodynamic element was 0–5.…”