A unified AI framework for solubility prediction across organic solvents
Vassileiou, Antony D. and Robertson, Murray N. and Wareham, Bruce G. and Soundaranathan, Mithushan and Ottoboni, Sara and Florence, Alastair J. and Hartwig, Thoralf and Johnston, Blair F. (2022) A unified AI framework for solubility prediction across organic solvents. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.
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Abstract
We report on the use of a single, unified dataset for machine learning (ML)-driven solubility prediction across the chemical space. This was a departure from the solvent-specific datasets more commonly used.
ORCID iDs
Vassileiou, Antony D. ORCID: https://orcid.org/0000-0001-8146-8972, Robertson, Murray N. ORCID: https://orcid.org/0000-0001-9543-7667, Wareham, Bruce G. ORCID: https://orcid.org/0000-0002-8732-5013, Soundaranathan, Mithushan, Ottoboni, Sara ORCID: https://orcid.org/0000-0002-2792-3011, Florence, Alastair J. ORCID: https://orcid.org/0000-0002-9706-8364, Hartwig, Thoralf and Johnston, Blair F. ORCID: https://orcid.org/0000-0001-9785-6822;Persistent Identifier
https://doi.org/10.17868/strath.00081661-
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Item type: Conference or Workshop Item(Poster) ID code: 81661 Dates: DateEvent16 May 2022PublishedSubjects: Medicine > Therapeutics. Pharmacology Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Faculty of Engineering > Chemical and Process Engineering
Strategic Research Themes > Advanced Manufacturing and Materials
Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)Depositing user: Pure Administrator Date deposited: 02 Aug 2022 15:36 Last modified: 11 Nov 2024 17:06 URI: https://strathprints.strath.ac.uk/id/eprint/81661
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