User Friendly Drug Interaction Checker

The Drug Interaction Checker is made to enhance patient’s safety by providing reliable and quick drug synergy information. Developed with Python's Tkinter library, the application features a use rfriendly interface, real time drug name suggestions, and clear result displays, making it efficient and accessible for both healthcare patients and professional. This tool aims to reduce medication problem and better patient outcomes by giving easy access to critical drug synergy data. A drug to drug interaction, it uses Graph Convolution Networks (GCN) and Deep Neural Networks (DNN) to predict potential drugs interactions. GCN captures the topological relationships of drugs within the system, while the DNN model uses these relationships to predict interactions. By integrating these advanced AI techniques with the userfriendly application, this tool promises to be an efficient resource in clinical settings, significantly enhancing patient’s safety and reducing the risks associated with drug synergy.

Authors

Institutions

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-30
DOI
https://doi.org/10.22214/ijraset.2025.71642
Primary Topic
Medication Adherence and Compliance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

User Friendly Drug Interaction Checker

Manu Pratap Singh
International Journal for Research in Applied Science and Engineering Technology
Medication Adherence and Compliance
article

User Friendly Drug Interaction Checker

Manu Pratap Singh
article en

Abstract

The Drug Interaction Checker is made to enhance patient’s safety by providing reliable and quick drug synergy information. Developed with Python's Tkinter library, the application features a use rfriendly interface, real time drug name suggestions, and clear result displays, making it efficient and accessible for both healthcare patients and professional. This tool aims to reduce medication problem and better patient outcomes by giving easy access to critical drug synergy data. A drug to drug interaction, it uses Graph Convolution Networks (GCN) and Deep Neural Networks (DNN) to predict potential drugs interactions. GCN captures the topological relationships of drugs within the system, while the DNN model uses these relationships to predict interactions. By integrating these advanced AI techniques with the userfriendly application, this tool promises to be an efficient resource in clinical settings, significantly enhancing patient’s safety and reducing the risks associated with drug synergy.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Galgotias University (IN)
Openalex Percentile: Top 86%
Medication Adherence and Compliance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.