International journal of intellectual advancements and research in engineering computations

Meimobian Gland Dysfunction Forecasting Based on Convolutional Neural Network

Subasri. R — 2025-07-13

Meimobian Gland Dysfunction (MGD) means the disease of dry eye, which is based on millions of the world. This project shows a computer-based vision system that uses U-NET for automated classification using U-Net Architecture. After successful login, the user can upload any eye image to analyze. The U-NET model is the process of sent photos to find the MGD marks. which shows the eye from MGD. This helps the eyes of the eye and health experts in MGD disease. The specified system is more accessible and used for clinical applications and Teledic. Using the study depth, the system is more powerful to analyze. The U-NET enrollment ensures the extraction and organization of the situation. It is compared to traditional conditions, which has been expected to indicate the truth with a speedy speed. Web boards send a distance access and effectively for a large -scale analysis. Mostly, this system is a major progress in the analysis of the mGD, control early findings and patient results.

The Intersection of Generative AI and Intellectual Property: Rethinking Fair Dealing for Text and Data Mining in Pharmaceuticals

Ravi Kumar Kota — 2025-09-28

The rapid advancement of generative artificial intelligence (AI) has revolutionized text and data mining (TDM) practices in the pharmaceutical industry, raising critical questions about intellectual property (IP) protection and the fair dealing doctrine. This article examines the intersection of generative AI and IP, focusing on challenges related to data ownership, licensing, and regulatory compliance. It highlights the need for updated legal frameworks to balance innovation with IP rights, particularly in drug discovery, clinical trials, and personalized medicine. The study provides recommendations for policy reforms, ethical AI use, and international harmonization to support responsible innovation in pharmaceutical regulatory affairs.

Molecular Docking Studies of Natural Compounds Against Cancer Targets

P. Aravanan — 2025-10-31

Cancer is one of the most common and deadly illnesses in the world, researchers are constantly looking for new treatment options with less adverse effects. Because of their varied bioactivities and often low toxicity, natural chemicals made from medicinal plants present intriguing substitutes. The interaction of specific phytochemicals, such as curcumin, quercetin, resveratrol, and berberine, with important cancer targets, such as the vascular endothelial growth factor receptor (VEGFR), B-cell lymphoma 2 (BCL-2), and epidermal growth factor receptor (EGFR), was examined in this study using molecular docking. Auto Dock Vina was used to run docking simulations, and interaction patterns and binding affinities were examined. The compound quercetin had the highest binding affinity for BCL-2, suggesting that it may play a part in triggering apoptosis. All of the chosen compounds showed positive interactions with important residues at the active sites of their respective targets, including hydrophobic and hydrogen bonding interactions. The potential of natural chemicals as lead structures for the creation of anticancer drugs is supported by these findings. This in silico study adds to the expanding field of green pharmacy and natural product based cancer therapeutics and offers a foundation for additional pharmacological assessments.