ArticleNature communications2020
Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
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The trial behind it
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Who cites it
77 citing papers in PubMed.
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights.Communications engineering · 2026Article
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes.The journal of physical chemistry. B · 2026Article
- Development and evaluation of an effective solubility prediction model for pharmaceuticals in organic solvents using machine learning based on eXtreme Gradient Boosting.Scientific reports · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- When Does Machine Learning Add Value over Theory? Predicting API Solubility in Binary Mixtures with COSMO-RS and DOOIT2 Across Diverse and Homogeneous Systems.Molecules (Basel, Switzerland) · 2026Article
- Combining PC-SAFT and ML to Access Unknown API Solubilities.Molecular pharmaceutics · 2026Article
- Navigating the Solubility Landscape of APIs in Deep Eutectic Solvents: A Data-Driven Thermodynamic Taxonomy of Solvation Regimes and Mechanisms.Molecules (Basel, Switzerland) · 2026Article
- Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures.Scientific reports · 2026Article
- Dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.Scientific data · 2026Article
- Article
- Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space.Journal of chemical information and modeling · 2026Article
- Rethinking the AI Paradigm for Solubility Prediction of Drug‑Like Compounds with Dual-Perspective Modeling and Experimental Validation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach.Scientific reports · 2025Article
- Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering.Molecules (Basel, Switzerland) · 2025Article
- A Solvent Selection Framework for Porous Organic Polymers.Journal of chemical information and modeling · 2025Article
- Accelerated discovery of new organic photovoltaic dyes for OPVs using light absorbance as the primary screening criterionRSC advances · 2025Article
- Duality of Simplicity and Accuracy in QSPR: A Machine Learning Framework for Predicting Solubility of Selected Pharmaceutical Acids in Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2025Article
- Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.
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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.