ArticleJournal of cheminformatics2021
The effect of noise on the predictive limit of QSAR models.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization.Journal of chemical information and modeling · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.Journal of chemical information and modeling · 2025Article
- A Review of In Silico and In Vitro Approaches in the Fight Against Carbapenem-Resistant Enterobacterales.Journal of clinical laboratory analysis · 2025Review
- Beware the Little Foxes that Spoil the Vines: Small Inconsistencies in Clinical Data Can Distort Machine Learning Findings.Fortune journal of health sciences · 2025Article
- Development and Evaluation of Conformal Prediction Methods for Quantitative Structure-Activity Relationship.ACS omega · 2024Article
- Reducing overconfident errors in molecular property classification using Posterior Network.Patterns (New York, N.Y.) · 2024Article
- Article
- Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.Journal of chemical information and modeling · 2024Article
- Article
- Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling.Journal of cheminformatics · 2024Article
- Designing solvent systems using self-evolving solubility databases and graph neural networks.Chemical science · 2024Article
- Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.Current drug research reviews · 2024Review
- A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels.bioRxiv : the preprint server for biology · 2023Article
- ProfhEX: AI-based platform for small molecules liability profiling.Journal of cheminformatics · 2023Article
- A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants.SAR and QSAR in environmental research · 2023Article
- Batched Bayesian Optimization for Drug Design in Noisy Environments.Journal of chemical information and modeling · 2022Article
- Review
- The use of predictive models to develop chromatography-based purification processes.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology.
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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.