Year: 2025
Type: article
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.
Source: Nature Communications
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: 2021 BUDGET REQUEST FOR THE DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT, G2900401177, 2021 BUDGET REQUEST FOR THE DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT, 2022 COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP PROGRAM, 2022 COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP PROGRAM, G824380962
OpenAlex ID: W4413306774