Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

With the development of neural network technology and the rapid growth of China's tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status, tourism development investment, and sustainability evaluation in the tourism area. The multispecies evolutionary genetic algorithm is used for comprehensive analysis and evaluation. The algorithm can realize the complex analysis and comprehensive evaluation of the core influencing factors of neural network. Accurate analysis and evaluation were carried out according to the different characteristics of tourism resources and the current situation of tourism income. The results show that the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm has the advantages of high practicability, good sorting effect of variable ratio, and good data integration. It can effectively analyze and compare the comprehensive evaluation factors affecting tourism resources in different ratios.

Identification of Novel Protein Kinase Receptor Type 2 Inhibitors Using Pharmacophore and Structure-Based Virtual Screening

The Protein Kinase Receptor type 2 (RIPK2) plays an important role in the pathogenesis of inflammatory diseases; it signals downstream of the NOD1 and NOD2 intracellular sensors and promotes a productive inflammatory response. However, excessive NOD2 signaling has been associated with various diseases, including sarcoidosis and inflammatory arthritis; the pharmacological inhibition of RIPK2 is an affinity strategy that demonstrates an increased expression of pro-inflammatory secretion activity. In this study, a pharmacophoric model based on the crystallographic pose of ponatinib, a potent RIPK2 inhibitor, and 30 other ones selected from the BindingDB repository database, was built. Compounds were selected based on the available ZINC compounds database and in silico predictions of their pharmacokinetic, toxicity and potential biological activity. Molecular docking was performed to identify the probable interactions of the compounds as well as their binding affinity with RIPK2. The compounds were analyzed to ponatinib and WEHI-345, which also used as a control. At least one of the compounds exhibited suitable pharmacokinetic properties, low toxicity and an interesting binding affinity and high fitness compared with the crystallographic pose of WEHI-345 in complex with RIPK2. This compound also possessed suitable synthetic accessibility, rendering it a potential and very promising RIPK2 inhibitor to be further investigated in regards to different diseases, particularly inflammatory ones.

Combined pharmacophore modeling, 3D-QSAR, homology modeling and docking studies on CYP11B1 inhibitors

The mitochondrial cytochrome P450 enzymes inhibitor steroid 11β-hydroxylase (CYP11B1) can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing’s syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) was used to align the compounds and perform comparative molecular field analysis (CoMFA) with Q² = 0.658, R² = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study based on the alignment with the atom root mean square (RMS) was applied using comparative molecular field analysis (CoMFA) with Q² = 0.666, R² = 0.978, and comparative molecular similarity indices analysis (CoMSIA) with Q² = 0.721, R² = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D) model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ). In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing’s syndrome treatment.