ArticleADMET & DMPK2020
Three machine learning models for the 2019 Solubility Challenge.
Article in ADMET & DMPK, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- CrystalClear: an open, modular protocol for predicting molecular crystal growth from solution.Chemical science · 2023Article
- Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review.Metabolites · 2023Review
- Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.Pharmaceutics · 2022Article
- Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.Biosensors · 2022Article
- Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.Pharmaceutics · 2021Article
- Strategies of solubility enhancement and perspectives in solubility measurements of pharmaceutical compounds.ADMET & DMPK · 2020Article
Corrections and comments
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1 author.
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Abstract
We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds.
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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.