ArticleJournal of computer-aided molecular design2007
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
Article in Journal of computer-aided molecular design, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- 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
- Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.Journal of chemical information and modeling · 2025Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Machine learning for flow batteries: opportunities and challenges.Chemical science · 2022Review
- Artificial Intelligence in Compound Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression.Molecules (Basel, Switzerland) · 2021Article
- Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.Molecules (Basel, Switzerland) · 2021Article
- Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.Pharmaceutics · 2021Article
- Article
- Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database.ADMET & DMPK · 2020Article
- Can small drugs predict the intrinsic aqueous solubility of 'beyond Rule of 5' big drugs?ADMET & DMPK · 2020Article
- ADME properties evaluation in drug discovery: in silico prediction of blood-brain partitioning.Molecular diversity · 2018Article
- Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.Journal of chemical information and modeling · 2013Article
- Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.Journal of computer-aided molecular designArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.
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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.