Quantum vs Classical Machine Learning for Rare Disease Drug Repurposing
Exploring how AI and quantum machine learning can help identify existing drugs that may be repurposed to treat rare diseases, while comparing quantum and classical approaches.
Rare diseases affect millions of people worldwide, yet many have limited or no effective treatments. Developing a new drug can take many years and require significant resources. Drug repurposing offers an alternative approach by identifying existing or abandoned drugs that may be useful for treating other diseases.
This article explores how Artificial Intelligence and Quantum Machine Learning can be applied to rare-disease drug repurposing. The proposed approach combines biological and molecular data with classical machine learning models and quantum machine learning methods to identify potential drug–disease relationships.
The study also focuses on comparing classical and quantum approaches using appropriate evaluation methods rather than assuming that quantum computing is automatically better. The generated candidates are considered research hypotheses that require further biological and clinical validation.
AWS services can provide scalable infrastructure for data processing, model training, experimentation, and deployment of such a pipeline.
This article explores how Artificial Intelligence and Quantum Machine Learning can be applied to rare-disease drug repurposing. The proposed approach combines biological and molecular data with classical machine learning models and quantum machine learning methods to identify potential drug–disease relationships.
The study also focuses on comparing classical and quantum approaches using appropriate evaluation methods rather than assuming that quantum computing is automatically better. The generated candidates are considered research hypotheses that require further biological and clinical validation.
AWS services can provide scalable infrastructure for data processing, model training, experimentation, and deployment of such a pipeline.
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