Graph-Based Natural Language Processing for the Pharmaceutical Industry

Dumitriu, Alexandra; Molony, Cliona; Daluwatte, Chathuri

doi:10.1007/978-3-030-67681-0_6

Cited by 2 publications

(2 citation statements)

References 88 publications

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“…By analyzing the literature, AI models can suggest drug candidates that have a demonstrated efficacy against specific diseases or targets, leading to drug repurposing opportunities and exploring the concept of polypharmacology [79]. AI models can integrate diverse data sources, such as clinical trial results, genomic data, and chemical databases, to build knowledge graphs [80]. Knowledge graphs represent complex relationships between drugs, targets, diseases, and biological pathways, facilitating comprehensive analysis and hypothesis generation [80].…”

Section: (V) Text Miningmentioning

confidence: 99%

“…AI models can integrate diverse data sources, such as clinical trial results, genomic data, and chemical databases, to build knowledge graphs [80]. Knowledge graphs represent complex relationships between drugs, targets, diseases, and biological pathways, facilitating comprehensive analysis and hypothesis generation [80]. Also, AI models can mine the literature to identify the adverse drug reactions reported in clinical studies and post-marketing surveillance.…”

Section: (V) Text Miningmentioning

confidence: 99%

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Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

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Section: (V) Text Miningmentioning

confidence: 99%

Section: (V) Text Miningmentioning

confidence: 99%

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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Review of Natural Language Processing in Pharmacology

et al. 2023

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Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. Significance StatementThe main objective of this work is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.

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Graph-Based Natural Language Processing for the Pharmaceutical Industry

Cited by 2 publications

References 88 publications

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Review of Natural Language Processing in Pharmacology

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