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All Journal Journal of Applied Pharmaceutical Research Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding
Morsu, Ashok
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Medicine & Pharmacology Public Health Social Sciences

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In silico assessment of flavonoids from Matricaria chamomilla for anti-psoriatic potential via molecular docking and ADME/T profiling V. V. Rajesham; Pentu, Narendra; Kumar, Pasupuleti Kishore; T. Rama Rao; Morsu, Ashok
Journal of Applied Pharmaceutical Research Vol. 13 No. 4 (2025)
Publisher : Creative Pharma Assent

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69857/joapr.v13i4.1309

Abstract

Background: Computational tools are advancing in the drug discovery process to assess the safety profiles of new compounds with reduced investment. Herbal remedies exhibit a diverse range of active compounds that can alleviate various disease conditions with fewer side effects. Method: This study investigates the molecular docking of phytochemicals from Matricaria Chamomilla against inflammation-induced skin disorders, such as psoriasis. Using AutoDock Vina and MGL Tools, key compounds were evaluated for binding affinity with target proteins. ADMET analysis, as assessed by pkCSM and SWISSADME, to predict the Lipinski’s Rule of Five. Redocking was implemented to confirm the binding affinity of the docked position. Results: This molecular docking of phenolic compounds and flavonoids, including quercetin, apigenin, rutin, luteolin, and various glycosylated derivatives—from Matricaria Chamomilla against cellular proteins implicated in psoriasis (PDE-4, p38MAPK, IL-23, BTK, JAK-3, TNF-α, IL-17A, and IL-6). Using Autodock Vina and MGL Tools, rutin and quercetin demonstrated favourable binding affinities. At the same time, luteolin-7-glycoside exhibited the highest docking scores (e.g., -10.8 kcal/mol for PDE-4, -9.7 kcal/mol for JAK-3, and -9.1 kcal/mol for TNF-α) compared to the standard. Results highlight the potential of chamomile phytochemicals as safe, orally effective agents for managing inflammatory skin conditions. Redocking confirms the RMSD values are within the limits of < 2 A0. Conclusion: The data suggest that chamomile flavonoids could be safe and beneficial for treating inflammatory diseases and psoriasis. Although enzymatic and cell-based assays, along with further preclinical evaluations, are essential for advancing research in disease modification, formulation strategies play a role in improving drug characteristics
The Impact of Generative AI on Clinical Decision-Support Systems: A Systematic Review of Applications, Benefits, and Ethical Challenges Polampally, Sanjay Kumar; Ravindran, Renjith Kathalikkattil; Jeyabalan, Jeyashree; Kusam, Venugopala Reddy; Morsu, Ashok; Ragunayakula, Sharat Kumar
Proceedings of Universitas Muhammadiyah Yogyakarta Graduate Conference Vol. 5 No. 1 (2025): Fostering Gen Z for Sustainable Development and Renewable Energy
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/grace.v5i1.685

Abstract

This systematic literature review explores the integrations of generative artificial intelligence (AI) models, such as ChatGPT, GPT-4, MedPaLM, BioGPT, and LLaMA, into Clinical Decision Support Systems (CDSS) between 2020 and 2025. Analyzing 500 studies across multiple countries and healthcare contexts, the paper evaluates that generative AI transforms clinical workflows through enhanced diagnostic assistance, personalized treatment planning, improved patient communication, and document automation."This systematic literature review provides the first comprehensive analysis of generative artificial intelligence (AI) integration into Clinical Decision Support Systems (CDSS) from 2020 to 2025, addressing a critical gap in understanding their transformative impact on healthcare delivery. Through rigorous PRISMA-guided analysis of 500 peer-reviewed studies across six continents, we establish a novel theoretical framework demonstrating how generative AI models (ChatGPT, GPT-4, MedPaLM, BioGPT, and LLaMA) fundamentally reshape clinical workflows through four primary mechanisms: enhanced diagnostic reasoning, personalized treatment optimization, adaptive patient communication, and intelligent documentation automation.
Co-Authors Jeyabalan, Jeyashree Kumar, Pasupuleti Kishore Kusam, Venugopala Reddy Pentu, Narendra Polampally, Sanjay Kumar Ragunayakula, Sharat Kumar Ravindran, Renjith Kathalikkattil T. Rama Rao V. V. Rajesham