Rule Based Medicine Recommendation for Skin Diseases Using Ontology with Semantic Information

Generally, skin diseases are taken seriously only after it gets aggravated. Many patients do not feel comfortable to consult physician for their skin problems and try to cure with home remedies. Identifying good dermatologist is more challenging. Disease gets cured with ease, if treated properly otherwise it becomes complicated. To handle this, knowledge-based decision support system is developed which provide recommendation for treatment. System realizes patient’s symptoms with location, cause and recommends appropriate medicines. Ontology is used to elaborate data concepts and their relationships in skin disease, which permits sharing and reuse of domain knowledge. Semantics described using Web Ontology Language (OWL) with vocabularies, resources, logic’s, and inference rules are queried for relevant information through SPARQL. System proved to render most valuable service to skin patients with ease and accuracy of its recommendation is high. It renders best treatment in COVID situation where people’s movement is restricted to great extent. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In silico screening of phytoconstituents with antiviral activities against sars-cov-2 main protease, nsp12 polymerase, and nsp13 helicase proteins

Aims: The present study aimed to analyse the molecular interactions of the phytoconstituents known for their antiviral activity with the SARS-CoV-2 nonstructural proteins such as main protease (6LU7), Nsp12 polymerase (6M71), and Nsp13 helicase (6JYT). The applied in silico methodologies were molecular docking and pharmacophore modeling using Schrodinger software. Methods: The phytoconstituents were taken from PubChem, and SARS-CoV-2 proteins were downloaded from the protein data bank. The molecular interactions, binding energy, ADMET properties, and pharmacophoric features were analysed by glide XP, prime MM-GBSA, qikprop, and phase application of Schrodinger, respectively. The antiviral activity of the selected phytoconstituents was carried out by PASS predictor online tools. Results: The docking score analysis showed that quercetin 3-rhamnoside (-8.77 kcal/mol) and quercetin 3-rhamnoside (-7.89 kcal/mol) were excellent products to bind with their respective targets such as 6LU7, 6M71, and 6JYT. The generated pharmacophore hypothesis model validated the docking results, confirming the hydrogen bonding interactions of the amino acids. The PASS online tool predicted constituent's antiviral potentials. Conclusion: The docked phytoconstituents showed excellent interactions with the SARS-CoV-2 proteins, and on the outset, quercetin 3-rhamnoside and quercetin 7-rhamnoside interacted well with all the three proteins;these belong to the plant Houttuynia cordata. The pharmacophore hypothesis has revealed the characteristic features responsible for their interactions, and PASS prediction data has supported their antiviral activities. Thus, these natural compounds could be developed as lead molecules for antiviral treatment against SARS-CoV-2. Further in-vitro and in-vivo studies could be carried out to provide better drug therapy. ©2021 Bentham Science Publishers.