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.

[thumbnail of Vassileiou-etal-CMAC-2022-A-unified-AI-framework-for-solubility-prediction]
Preview
Text. Filename: Vassileiou_etal_CMAC_2022_A_unified_AI_framework_for_solubility_prediction.pdf
Final Published Version
License: Strathprints license 1.0

Download (1MB)| Preview

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 logoORCID: https://orcid.org/0000-0001-8146-8972, Robertson, Murray N. ORCID logoORCID: https://orcid.org/0000-0001-9543-7667, Wareham, Bruce G. ORCID logoORCID: https://orcid.org/0000-0002-8732-5013, Soundaranathan, Mithushan, Ottoboni, Sara ORCID logoORCID: https://orcid.org/0000-0002-2792-3011, Florence, Alastair J. ORCID logoORCID: https://orcid.org/0000-0002-9706-8364, Hartwig, Thoralf and Johnston, Blair F. ORCID logoORCID: https://orcid.org/0000-0001-9785-6822;