A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents
Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems.
Authors
- B. Johnston
- Morgan R. Alexander (ORCID: https://orcid.org/0000-0001-5182-493X)
- Ricky D. Wildman (ORCID: https://orcid.org/0000-0003-2329-8471)
- Isabel Cooley (ORCID: https://orcid.org/0009-0005-8663-1157)
- Anna K. Croft (ORCID: https://orcid.org/0000-0001-5330-150X)
- W.J. Wang (ORCID: https://orcid.org/0000-0001-6111-6945)
Institutions
- University of Nottingham (GB)
- Loughborough University (GB)
- University of Strathclyde (GB)
Publication Details
- Journal
- Digital Discovery
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1039/d5dd00456j
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 16.08
Funders
- Engineering and Physical Sciences Research Council