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How can natural language processing help model informed drug development?: a review.

Roopal Bhatnagar1, Sakshi Sardar2, Maedeh Beheshti2

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|June 15, 2022
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Summary
This summary is machine-generated.

Natural language processing (NLP) aids model-informed drug development (MIDD) across discovery, trials, and safety. While transformer models and libraries like Hugging Face offer powerful tools, challenges in reproducibility and data limitations hinder wider adoption.

Keywords:
NLPdeep learningdrug developmentmachine learning

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Natural Language Processing (NLP) is increasingly applied in drug development.
  • Model-Informed Drug Development (MIDD) leverages data and computational approaches to optimize drug design and evaluation.

Purpose of the Study:

  • To summarize current applications of NLP in MIDD.
  • To identify areas for improvement in NLP for MIDD.

Main Methods:

  • A literature review of publications from PubMed and Google Scholar.
  • Analysis of NLP libraries and models from websites and GitHub.
  • Stratification of NLP applications into drug discovery, clinical trials, and pharmacovigilance stages.

Main Results:

  • NLP facilitates gene-disease mapping, biomarker discovery, patient-trial matching, and adverse event detection.
  • Key NLP functionalities include named entity recognition, word embeddings, and relation extraction, often implemented with transformer models.
  • Libraries like Hugging Face and platforms such as DisGeNet enable NLP integration in MIDD.

Conclusions:

  • NLP offers significant potential in MIDD, with current applications spanning the drug development lifecycle.
  • Addressing challenges like reproducibility, explainability, and data limitations is crucial for broader NLP adoption in MIDD.