ArticleNature communications2025
Data-driven organic solubility prediction at the limit of aleatoric uncertainty.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes.The journal of physical chemistry. B · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- Dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.Scientific data · 2026Article
- Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space.Journal of chemical information and modeling · 2026Article
- Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design.Industrial & engineering chemistry research · 2026Article
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- A diverse and chemically relevant solvation model benchmark set with flexible molecules and conformer ensembles.Chemical science · 2025Article
- Data-driven organic solubility prediction at the limit of aleatoric uncertainty.Nature communications · 2025Article
- BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.