Prediction of Drug Bioactivity in Alzheimer’s Disease Using Machine Learning Techniques and Community Networks

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Hemkiran, S (2022) Prediction of Drug Bioactivity in Alzheimer’s Disease Using Machine Learning Techniques and Community Networks. Current Bioinformatics, 17 (8). pp. 698-709. ISSN 15748936

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Abstract

The design of novel drugs is vital to combat fatal diseases such as Alzheimer’s. With quantum advances in computational methods, artificial intelligence (AI) techniques have been widely utilized in drug discovery. Since drug design is a protracted and resource-intensive process, extensive research is necessary for building predictive in-silico models to discover new medications for Alzheimer’s. A thorough analysis of models is, therefore, required to expedite the discovery of new drugs.

Item Type: Article
Uncontrolled Keywords: Alzheimer’s disease; Drug bioactivity prediction; LSTM-RNN; bioactivity network; deep neural networks; machine learning; regression
Subjects: A Artificial Intelligence and Data Science > Machine Learning
G Chemistry > Acids and bases
Divisions: Computer Science and Engineering
Depositing User: Users 5 not found.
Date Deposited: 13 May 2024 10:06
Last Modified: 13 May 2024 10:06
URI: https://ir.psgitech.ac.in/id/eprint/549

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