Automatic extraction of pharmaceutical manufacturing data from patents using Natural Language Processing (NLP)

Research output: Contribution to conferencePoster

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Abstract

Introduction
• Deep generative models (DGM) are models capable of generating realistic samples and learning hidden information
• DGM used in drug discovery to generate new molecular entities with desirable biological and chemical properties
• Applications in pharmaceutical manufacturing have not been fully explored
• Potential Benefits of DGM
- Aid process design by generating a feasible chain of unit operations for the production of an API/dosage forms
- Improve process understanding through the utilisation of latent variables that may be correlated to process parameters.
• Thousands of data are required to develop a model
• No database that consolidates this information available in literature to be used in DGM for primary or secondary manufacturing domain
Original languageEnglish
Pages27-27
Number of pages1
Publication statusPublished - 16 May 2022
EventCMAC Annual Open Day 2022 - Glasgow, United Kingdom
Duration: 16 May 202218 May 2022

Conference

ConferenceCMAC Annual Open Day 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period16/05/2218/05/22

Keywords

  • Natural Language Processing (NLP)
  • pharmaceutical data
  • automatic extraction

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