ArticleJournal of computer-aided molecular design2013
QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality.
Article in Journal of computer-aided molecular design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Mathematical models for predicting the toxicity of micropollutant mixtures in water.Arhiv za higijenu rada i toksikologiju · 2025Review
- Development and Evaluation of Conformal Prediction Methods for Quantitative Structure-Activity Relationship.ACS omega · 2024Article
- The effect of noise on the predictive limit of QSAR models.Journal of cheminformatics · 2021Article
- Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression.Molecules (Basel, Switzerland) · 2021Article
- Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures.Molecules (Basel, Switzerland) · 2021Article
- QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds.Environmental science and pollution research international · 2020Article
- Time dependent analysis of assay comparability: a novel approach to understand intra- and inter-site variability over time.Journal of computer-aided molecular design · 2015Article
- Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?Journal of computer-aided molecular design · 2015Article
- Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling.Journal of cheminformatics · 2015Article
- How accurately can we predict the melting points of drug-like compounds?Journal of chemical information and modeling · 2014Article
- Using beta binomials to estimate classification uncertainty for ensemble models.Journal of cheminformatics · 2014Article
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Authors and funding
5 authors.
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Abstract
We propose that quantitative structure-activity relationship (QSAR) predictions should be explicitly represented as predictive (probability) distributions. If both predictions and experimental measurements are treated as probability distributions, the quality of a set of predictive distributions output by a model can be assessed with Kullback-Leibler (KL) divergence: a widely used information theoretic measure of the distance between two probability distributions. We have assessed a range of different machine learning algorithms and error estimation methods for producing predictive distributions with an analysis against three of AstraZeneca's global DMPK datasets. Using the KL-divergence framework, we have identified a few combinations of algorithms that produce accurate and valid compound-specific predictive distributions. These methods use reliability indices to assign predictive distributions to the predictions output by QSAR models so that reliable predictions have tight distributions and vice versa. Finally we show how valid predictive distributions can be used to estimate the probability that a test compound has properties that hit single- or multi- objective target profiles.
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