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
• 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 language | English |
|---|---|
| Pages | 27-27 |
| Number of pages | 1 |
| Publication status | Published - 16 May 2022 |
| Event | CMAC Annual Open Day 2022 - Glasgow, United Kingdom Duration: 16 May 2022 → 18 May 2022 |
Conference
| Conference | CMAC Annual Open Day 2022 |
|---|---|
| Country/Territory | United Kingdom |
| City | Glasgow |
| Period | 16/05/22 → 18/05/22 |
Keywords
- Natural Language Processing (NLP)
- pharmaceutical data
- automatic extraction