Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 33, 08 March 2024
Open
Access | Article
Transfer learning-enabled classification of drugs for Alzheimer’s Disease
* Author to whom correspondence should be addressed.
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease with a worldwide prevalence of 760.5 per 100,000 inhabitants. Environment factors and genetic predispositions can influence an individual’s risk of developing AD. Recent research provided insights into the genetic mechanisms of AD, but a comprehensive database of drug-like molecules that can exacerbate or reduce AD risk remains unavailable. Here, we use machine learning to create a similarity map that reveals new putative drugs structurally similar to existing compounds known to be targeting pathways relevant in AD. We trained an autoencoder on a large drug database of over 14,000 drugs, with features derived from several modalities including molecular fingerprints. We then computed similarity scores based on these reduced dimensions. We show that our model is able to identify new compounds structurally similar to existing drugs linked to AD. We conclude that our model holds the potential to elucidate new compounds based on structural similarity and can be used to identify new drugs that affect critical pathways in AD.
Keywords
Alzheimer’s Disease, drugs, risk factors, machine learning
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-323-4
- ISBN (Online)
- 978-1-83558-324-1
- Published Date
- 08 March 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/33/20240821
- Copyright
- 08 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited