Data-driven organic solubility prediction at the limit of aleatoric uncertainty
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 sol...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62717-7 |
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| _version_ | 1849226143941525504 |
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| author | Lucas Attia Jackson W. Burns Patrick S. Doyle William H. Green |
| author_facet | Lucas Attia Jackson W. Burns Patrick S. Doyle William H. Green |
| author_sort | Lucas Attia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86732046b8024a0ab71e91d1e23076c3 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-86732046b8024a0ab71e91d1e23076c32025-08-24T11:36:57ZengNature PortfolioNature Communications2041-17232025-08-0116111010.1038/s41467-025-62717-7Data-driven organic solubility prediction at the limit of aleatoric uncertaintyLucas Attia0Jackson W. Burns1Patrick S. Doyle2William H. Green3Department of Chemical Engineering, MITDepartment of Chemical Engineering, MITDepartment of Chemical Engineering, MITDepartment of Chemical Engineering, MITAbstract 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.https://doi.org/10.1038/s41467-025-62717-7 |
| spellingShingle | Lucas Attia Jackson W. Burns Patrick S. Doyle William H. Green Data-driven organic solubility prediction at the limit of aleatoric uncertainty Nature Communications |
| title | Data-driven organic solubility prediction at the limit of aleatoric uncertainty |
| title_full | Data-driven organic solubility prediction at the limit of aleatoric uncertainty |
| title_fullStr | Data-driven organic solubility prediction at the limit of aleatoric uncertainty |
| title_full_unstemmed | Data-driven organic solubility prediction at the limit of aleatoric uncertainty |
| title_short | Data-driven organic solubility prediction at the limit of aleatoric uncertainty |
| title_sort | data driven organic solubility prediction at the limit of aleatoric uncertainty |
| url | https://doi.org/10.1038/s41467-025-62717-7 |
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