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Data-driven organic solubility prediction at the limit of aleatoric uncertainty

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Listed:
  • Lucas Attia

    (MIT)

  • Jackson W. Burns

    (MIT)

  • Patrick S. Doyle

    (MIT)

  • William H. Green

    (MIT)

Abstract

Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process and existing methods for in silico estimation of solubility are limited by their generality, speed, and accuracy. This work presents two models derived from the FASTPROP and CHEMPROP architectures and trained on BigSolDB which are capable of predicting solubility at arbitrary temperatures for a wide range of small molecules in organic solvent. Both extrapolate to unseen solutes 2–3 times more accurately than the current state-of-the-art model and we demonstrate that they are approaching the aleatoric limit (0.5–1 $$\log S$$ log S ) of available test data, suggesting that further improvements in prediction accuracy require more accurate datasets. The FASTPROP-derived model (called FASTSOLV) and the CHEMPROP-based model are open source, freely accessible via a Python package and web interface, highly reproducible, and up to 2 orders of magnitude faster than current alternatives.

Suggested Citation

  • Lucas Attia & Jackson W. Burns & Patrick S. Doyle & William H. Green, 2025. "Data-driven organic solubility prediction at the limit of aleatoric uncertainty," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62717-7
    DOI: 10.1038/s41467-025-62717-7
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    References listed on IDEAS

    as
    1. Samuel Boobier & David R. J. Hose & A. John Blacker & Bao N. Nguyen, 2020. "Machine learning with physicochemical relationships: solubility prediction in organic solvents and water," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Banan Alhazmi & Gergo Ignacz & Maria Vincenzo & Mohamed Nejib Hedhili & Gyorgy Szekely & Suzana P. Nunes, 2024. "Ultraselective Macrocycle Membranes for Pharmaceutical Ingredients Separation in Organic Solvents," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
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