Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach

Diabetes mellitus is a severe condition that has the potential to impair strength. The disease known as diabetes mellitus, which is a chronic condition, is brought on by a significant rise in blood glucose levels. The diagnosis of this condition is made using a variety of chemical and physical testing. Diabetes, however, can harm the organs if it goes undetected. This study develops a hybrid deep-learning technique to recognize Type 2 diabetes mellitus. The data is cleaned up at the pre-processing stage using a data transformation technique based on the Yeo-Jhonson transformation. The tanimoto similarity is used in the feature selection process to select the best features from the data. To prepare data for future processing, data augmentation is performed. The Deep Residual Network and the Rider-based Neural Network are recommended and trained separately for the T2DM identification using the Competitive Multi-Verse Rider Optimizer. The outputs generated by the RideNN and DRN classifiers are blended using correlation-based fusion. The suggested CMVRO-based NN-DRN has shown improved performance with the highest accuracy of 91.4%, sensitivity of 94.8%, and specificity of 90.1%.

Exploring potential Plasmodium kinase inhibitors: a combined docking, MD and QSAR studies

Malaria is a disease caused mostly by Plasmodium falciparum, affects millions of people each year. The kinases are validated targets for malaria infection. In this study, we investigate for real and hypothetical compounds that can inhibit cyclic guanosine monophosphate (CGMP)-dependent protein kinase using molecular docking via combined similarity analysis, molecular dynamics simulations, quantitative structure activity relationship (QSAR). Using Tanimoto similarity scores, ∼8.4 million compounds were screened. Compounds that have at least 70% similarity are used in further analysis. These compounds are assessed by means of docking, MMBPSA, MMGBSA and ANI_LIE. Based on consensus of different free energy methods and docking we revealed two potential inhibitors that can be useful for treatment of malaria. Apart from screening of real compounds, we have also selected the 10 most plausible hypothetical compounds by performing QSAR. By QSAR proposed pharmacophores, we generated over 247 hypothetical compounds and among them 19 molecules with lower QSAR predicted IC50 values and high docking scores were selected for further analysis. We selected the top 10 inhibitor candidates and performed MD simulations for free energy calculations like the protocol applied for real compounds. According to the free energy calculations, we suggest 2 real (C34H29F5N8O4S and C30H27F2N7O2S2, PubChem IDs: 140564801 and 89035196, respectively) and 2 hypothetical (C23H27FN6O2S, MOL3 and C23H25FN6O2S, MOL4) compounds that can be effective inhibitors against the protein kinase of Plasmodium falciparum.Communicated by Ramaswamy H. Sarma.

Identification of Corosolic and Oleanolic Acids as Molecules Antagonizing the Human RORγT Nuclear Receptor Using the Calculated Fingerprints of the Molecular Similarity

RORγT is a protein product of the RORC gene belonging to the nuclear receptor subfamily of retinoic-acid-receptor-related orphan receptors (RORs). RORγT is preferentially expressed in Th17 lymphocytes and drives their differentiation from naive CD4+ cells and is involved in the regulation of the expression of numerous Th17-specific cytokines, such as IL-17. Because Th17 cells are implicated in the pathology of autoimmune diseases (e.g., psoriasis, inflammatory bowel disease, multiple sclerosis), RORγT, whose activity is regulated by ligands, has been recognized as a drug target in potential therapies against these diseases. The identification of such ligands is time-consuming and usually requires the screening of chemical libraries. Herein, using a Tanimoto similarity search, we found corosolic acid and other pentacyclic tritepenes in the library we previously screened as compounds highly similar to the RORγT inverse agonist ursolic acid. Furthermore, using gene reporter assays and Th17 lymphocytes, we distinguished compounds that exert stronger biological effects (ursolic, corosolic, and oleanolic acid) from those that are ineffective (asiatic and maslinic acids), providing evidence that such combinatorial methodology (in silico and experimental) might help wet screenings to achieve more accurate results, eliminating false negatives.