Application of artificial intelligence and machine learning in drug repurposing

Ghandikota, Sudhir K.; Jegga, Anil G.

doi:10.1016/bs.pmbts.2024.03.030

Cited by 2 publications

References 177 publications

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Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

Lange,

Martyn,

Gogarty

et al. 2024

Preprint

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We will develop a novel approach to drug repurposing, utilising Natural Language Processing (NLP) and Literature Based Discovery (LBD) techniques. This will present a simplified, accessible drug repurposing pipeline using Word2Vec embeddings trained on PubMed abstracts to identify potential new medications to be repurposed. We present this approach in the context of antipsychotics, but it could be repeated for any available medication. The research is structured in three stages: 1. Identification of candidate medications using Word2Vec algorithm trained on scientific literature. 2. Empirical testing of identified candidates using a large hospital dataset to explore protective effects against disease onset. 3. Validation of findings using a second, independent dataset to assess generalizability. This method addresses limitations in current machine learning-based drug repurposing approaches, including lack of external validation and limited accessibility. By leveraging Word2Vec's ability to capture semantic relationships between words, the study aims to uncover hidden connections in medical literature that may lead to novel therapeutic discoveries. The protocol emphasizes transparency and reproducibility, utilizing publicly available electronic health record (EHR) databases for validation. This approach allows for tangible results even for researchers with limited machine learning expertise, bridging the gap between biomedical and information systems communities.

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Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

Lange,

Martyn,

Gogarty

et al. 2024

Preprint

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Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’

Zhang,

Shi,

Kamel Boulos

2024

Future Internet

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The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented Generation (RAG) and prompt engineering, and their applications in improving diagnostic accuracy and educational utility. Despite the potential, these technologies present challenges, including bias, hallucinations, and the need for robust safety protocols. The paper also discusses the regulatory and ethical considerations necessary for integrating these models into mainstream healthcare. By examining current studies and developments, this paper aims to provide a comprehensive overview of the state of LLMs in medicine and highlight the future directions for research and application. The study concludes that while LLMs hold immense potential, their safe and effective integration into clinical practice requires rigorous testing, ongoing evaluation, and continuous collaboration among stakeholders.

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Application of artificial intelligence and machine learning in drug repurposing

Cited by 2 publications

References 177 publications

Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’

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