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 9 papers.
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Who cites it
9 citing papers in PubMed.
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- Comparative assessment of physics-based in silico methods to calculate relative solubilities.Journal of computer-aided molecular design · 2024Article
- pH-dependent solubility prediction for optimized drug absorption and compound uptake by plants.Journal of computer-aided molecular design · 2023Article
- General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.Journal of chemical information and modeling · 2021Article
- Pruned Machine Learning Models to Predict Aqueous Solubility.ACS omega · 2020Article
- In silico design and optimization of selective membranolytic anticancer peptides.Scientific reports · 2019Article
- Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes.Molecules (Basel, Switzerland) · 2018Article
- Estimation of the applicability domain of kernel-based machine learning models for virtual screening.Journal of cheminformatics · 2010Article
- DPRESS: Localizing estimates of predictive uncertainty.Journal of cheminformatics · 2009Article
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.
Indexed as
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18060505What OpenQuestion holds
Registered trials
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