ABSTRACT
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potential inhibitors from Traditional Chinese Medicine Database. The potent top 10 compounds were selected as candidates by Dock Score. In order to further screen the candidates, we used numbers of machine learning regression models containing support vector machines, bagging, random forest and other regression algorithms, as well as deep neural network models to predict the activity of the candidates. In addition, as a traditional method, 2D QSAR (multiple linear regression) and 3D QSAR methods are also applied. The AI methods got a better performance than the traditional 2D QSAR method. Moreover, we also built a framework composed of deep neural networks and transformer to predict the binding affinity of candidates and DPP4. Artificial intelligence methods and QSAR models illustrated the compound, 2007_4105, was a potent inhibitor. The 2007_4105 compound was finally validated by molecular dynamics simulations. Combining all the models and algorithms constructed and the results, Hypecoum leptocarpum might be a potential and effective medicine herb for the treatment of DM.
Subject(s)
Algorithms , Artificial Intelligence , Drug Design , Drug Discovery/methods , Hypoglycemic Agents/chemistry , Binding Sites , Dipeptidyl-Peptidase IV Inhibitors/chemistry , Dipeptidyl-Peptidase IV Inhibitors/pharmacology , Humans , Hydrogen Bonding , Hypoglycemic Agents/pharmacology , Machine Learning , Molecular Docking Simulation , Molecular Dynamics Simulation , Molecular Structure , Neural Networks, Computer , Protein Binding , Quantitative Structure-Activity Relationship , WorkflowABSTRACT
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug development, and it plays a key role in drug discovery. Based on network pharmacology and virtual screening, candidates were selected from the molecular database. Nine artificial intelligence algorithm models were used to further verify the candidates' potential. The 350 training results of the deep learning model showed higher credibility, and the R-square of the training set and test set of the optimal model reached 0.89 and 0.84, respectively. The random forest model has an R-square of 0.91 and a mean square error of only 0.003. The R-square of the Adaptive Boosting model and the Bagging model reached 0.92 and 0.88, respectively. Molecular dynamics simulation evaluated the stability of the ligand-protein complex and achieved good results. Artificial intelligence models had unearthed the promising candidates for STAT3 inhibitors, and the good performance of most models showed that they still had practical value on small data sets.
Subject(s)
Artificial Intelligence , Drug Discovery/methods , Leukemia, Myeloid, Acute/drug therapy , Databases, Chemical , Humans , Leukemia, Myeloid, Acute/prevention & control , Ligands , Machine Learning , Molecular Dynamics Simulation , Protein Binding , STAT3 Transcription Factor/antagonists & inhibitorsABSTRACT
Previous studies have shown that small molecule inhibitors of NLRP3 may be a potential treatment for Parkinson's disease (PD). NACHT, LRR and PYD domains-containing protein 3 (NLRP3), heat shock protein HSP 90-beta (HSP90AB1), caspase-1 (CASP1) and cellular tumor antigen p53 (TP53) have significant involvement in the pathogenesis pathway of PD. Molecular docking was used to screen the traditional Chinese medicine database TCM Database@Taiwan. Top traditional Chinese medicine (TCM) compounds with high affinities based on Dock Score were selected to form the drug-target interaction network to investigate potential candidates targeting NLRP3, HSP90AB1, CASP1, and TP53 proteins. Artificial intelligence model, 3D-Quantitative Structure-Activity Relationship (3D-QSAR) were constructed respectively utilizing training sets of inhibitors against the four proteins with known inhibitory activities (pIC50). The results showed that 2007_22057 (an indole derivative), 2007_22325 (a valine anhydride) and 2007_15317 (an indole derivative) might be a potential medicine formula for the treatment of PD. Then there are three candidate compounds identified by the result of molecular dynamics.
ABSTRACT
A series of (R)-2-phenyl-4,5-dihydrothiazole-4-carboxamide derivatives containing a diacylhydrazine moiety were designed and synthesized. Their structures were confirmed by melting points, 1H NMR, 13C NMR, and elemental analysis (EA). Their antifungal and insecticidal activities were evaluated. The antifungal activity result indicated that most title compounds against Cercospora arachidicola, Alternaria solani, Phytophthora capsici, and Physalospora piricola exhibited apparent antifungal activities at 50 mg/L, and better than chlorothalonil or carbendazim. The EC50 values of (R)-N'-benzoyl-2-(4-chlorophenyl)-4,5-dihydrothiazole-4-carbohydrazide (I-5) against six tested phytopathogenic fungi were comparable to those of chlorothalonil. The CoMSIA model showed that a proper hydrophilic group in the R1 position, as well as a proper hydrophilic and electron-donating group in the R2 position, could improve the antifungal activity against Physalospora piricola, which contributed to the further optimization of the structures. Meanwhile, most title compounds displayed good insecticidal activities, especially compound (R)-N'-(4-nitrobenzoyl)-2-(4-nitrophenyl)-4,5-dihydrothiazole-4-carbohydrazide (III-3). The insecticidal mechanism results indicated that compound III-3 can serve as effective insect Ca2+ level modulators by disrupting the cellular calcium homeostasis in Mythimna separata.
