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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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Volume Published : 11
Issue Published : 119
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| Paper Title: | Medicure - Drug Discovery- Machine Learning-Based Prediction for Accelerating Drug Discovery and Target Interaction Analysis |
| Authors Name: | Janagama Adarsh , Kethavath Saiteja , Kotha Teja Prathap , Dr. Sanabam Bineshwor Singh |
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IJRTI_210929
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| Published Paper Id: | IJRTI2604099 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Drug discovery plays a vital role in the advancement of healthcare and the development of effective treatments for various diseases. However, traditional drug discovery processes are time-consuming, expensive, and require extensive experimental validation, often leading to delays in identifying potential drug candidates. Accurate prediction of compound bioactivity is essential for accelerating early-stage drug development and reducing research costs. This research presents a Machine Learning–based Drug Discovery and Bioactivity Prediction System named medicure, designed to assist researchers in making data-driven decisions during the drug screening process. The proposed system utilizes machine learning algorithms such as Random Forest to analyze chemical datasets derived from molecular descriptors generated using smiles representations. The system performs data pre-processing, feature engineering, and model training to predict the bioactivity of chemical compounds and identify promising drug candidates. By integrating cheminformatics with predictive analytics through a user-friendly stream lit web interface, the proposed platform enables rapid in silico screening, improves research efficiency, and reduces dependency on costly laboratory experiments. Experimental results indicate that the Random Forest algorithm provides effective and reliable prediction performance. The system demonstrates the potential of machine learning technologies in accelerating drug discovery and promoting intelligent pharmaceutical research. |
| Keywords: | Machine Learning, Machine Learning, Drug Discovery, Bioactivity Prediction, Random Forest, Cheminformatics, SMILES, streamlit, Predictive Analytics |
| Cite Article: | "Medicure - Drug Discovery- Machine Learning-Based Prediction for Accelerating Drug Discovery and Target Interaction Analysis ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a694-a704, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604099.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604099
Registration ID:210929
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a694-a704 Country: Hyderabad, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604099 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604099 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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