A unified AI framework for solubility prediction across organic solvents
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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;-
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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: 12 Dec 2024 16:40 URI: https://strathprints.strath.ac.uk/id/eprint/81661
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