Natural language processing in drug discovery: bridging the gap between text and therapeutics with artificial...

Area of Science:

• Life Sciences
• Drug Discovery
• Bioinformatics

Background:

• The exponential growth of scientific data presents both opportunities and challenges for drug discovery.
• Advancements in Artificial Intelligence (AI) have significantly enhanced Natural Language Processing (NLP) capabilities for analyzing scientific texts.
• Machine Learning (ML) techniques are increasingly adopted to innovate and accelerate drug discovery pipelines.

Purpose of the Study:

• To provide an overview of NLP techniques applicable to drug discovery.
• To explore the evolution of NLP from Named Entity Recognition (NER) to Large Language Models (LLMs).
• To discuss the strengths and weaknesses of various NLP methods in the context of drug discovery.

Main Methods:

• Literature review using Europe PMC, ResearchRabbit, and SciSpace.
• Exploration of Named Entity Recognition (NER) using ontologies and ML models.
• Investigation of Knowledge Graphs (KGs) for biological data management.
• Analysis of Large Language Models (LLMs) for complex data handling.

Main Results:

• NLP, powered by AI/ML, offers powerful tools for extracting and analyzing data from scientific literature.
• Different NLP methods, including NER, KGs, and LLMs, have distinct strengths and weaknesses.
• The choice of NLP method should be tailored to specific use cases and available data.

Conclusions:

• AI-driven NLP is transforming drug discovery by enabling efficient data analysis.
• Understanding the nuances of various NLP techniques is essential for researchers.
• Strategic selection of NLP tools is key to leveraging big data for pharmaceutical innovation.