Natural language processing in drug discovery: bridging the gap between text and therapeutics with artificial intelligence
The field of Natural Language Processing (NLP) within the life sciences has exploded in its capacity to aid the extraction and analysis of data from scientific texts in recent years through the advancement of Artificial Intelligence (AI). Drug discovery pipelines have been innovated and accelerated...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
30 Apr 2025
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| In: |
Expert opinion on drug discovery
Year: 2025, Volume: 20, Issue: 6, Pages: 765-783 |
| ISSN: | 1746-045X |
| DOI: | 10.1080/17460441.2025.2490835 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1080/17460441.2025.2490835 Verlag, kostenfrei, Volltext: https://www.tandfonline.com/doi/full/10.1080/17460441.2025.2490835 
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| Author Notes: | Christine Ann Withers, Amina Mardiyyah Rufai, Aravind Venkatesan, Santosh Tirunagari, Sebastian Lobentanzer, Melissa Harrison and Barbara Zdrazil |
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| 520 | |a The field of Natural Language Processing (NLP) within the life sciences has exploded in its capacity to aid the extraction and analysis of data from scientific texts in recent years through the advancement of Artificial Intelligence (AI). Drug discovery pipelines have been innovated and accelerated by the uptake of AI/Machine Learning (ML) techniques. The authors provide background on Named Entity Recognition (NER) in text - from tagging terms in text using ontologies to entity identification via ML models. They also explore the use of Knowledge Graphs (KGs) in biological data ingestion, manipulation, and extraction, leading into the modern age of Large Language Models (LLMs) and their ability to maneuver complex and abundant data. The authors also cover the main strengths and weaknesses of the many methods available when undertaking NLP tasks in drug discovery. Literature was derived from searches utilizing Europe PMC, ResearchRabbit and SciSpace. The mass of scientific data that is now produced each year is both a huge positive for potential innovation in drug discovery and a new hurdle for researchers to overcome. Notably, methods should be selected to fit a use case and the data available, as each method performs optimally under different conditions. | ||
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