Thesis
Towards addressing structural data limitations in machine learning for small molecule drug discovery
- Abstract:
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Drug discovery is an increasingly expensive and time-consuming process, with a drug taking over 10 years and $1.1 billion to bring to market on average. The recent rise of artificial intelligence and machine learning has promised to arrest and possibly reverse this trend. This thesis presents my work on analysing the impact of training data on these machine learning methods, specifically structure-based methods often used to enhance early-stage drug discovery and how I attempted to address th...
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- Files:
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(Preview, Dissemination version, pdf, 13.6MB, Terms of use)
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Authors
Contributors
+ Deane, C
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Statistics
- Role:
- Supervisor
+ Marsden, B
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- NDM
- Sub department:
- CMD
- Role:
- Supervisor
+ Boyles, F
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Doctoral Training Centre - MSD
- Role:
- Supervisor
+ Birchall, K
- Role:
- Supervisor
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/S024093/1
- Programme:
- SABS R^3 CDT
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Deposit date:
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2026-03-29
- ARK identifier:
Terms of use
- Copyright holder:
- Guy Durant
- Copyright date:
- 2025
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