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Related Experiment Video

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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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Modelling methods and cross-validation variants in QSAR: a multi-level analysis$.

A Rácz1, D Bajusz2, K Héberger1

  • 1a Plasma Chemistry Research Group , Research Centre for Natural Sciences, Hungarian Academy of Sciences , Budapest, Hungary.

SAR and QSAR in Environmental Research
|August 31, 2018
PubMed
Summary

Choosing the right cross-validation (CV) and modeling technique is crucial for accurate predictions. Support Vector Machines (SVM) with variable selection generally performed best, outperforming methods like Multiple Linear Regression (MLR).

Keywords:
ANNMLRPCRPLSQSARSRDSVMcross-validationtoxicityvalidation

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Area of Science:

  • Computational Chemistry
  • Toxicology
  • Machine Learning in Science

Background:

  • Prediction performance in quantitative structure-activity relationship (QSAR) studies is highly sensitive to the chosen cross-validation (CV) and model-building protocols.
  • Understanding the interplay between different CV variants and machine learning algorithms is essential for reliable model evaluation.

Purpose of the Study:

  • To investigate the impact of various cross-validation strategies (random, contiguous, Venetian blind, leave-one-out) on prediction performance.
  • To compare the effectiveness of different modeling techniques, including Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS) regression, Artificial Neural Networks (ANN), and Support Vector Machines (SVM).
  • To determine the influence of variable selection on model performance and its interaction with CV methods.

Main Methods:

  • Applied five-fold cross-validation (random, contiguous, Venetian blind) and leave-one-out cross-validation (CV) to acute toxicity datasets.
  • Generated predictive models using MLR, PCR, PLS, ANN, and SVM.
  • Evaluated and ranked methods using Sum of Ranking Differences (SRD) and factorial analysis of variance (ANOVA), alongside standard performance metrics (r², Q²).

Main Results:

  • Multiple Linear Regression (MLR) combined with contiguous block cross-validation exhibited the largest bias and variance.
  • Support Vector Machines (SVM) consistently outperformed other methods when compared against experimental values, especially with variable selection.
  • Venetian blind cross-validation emerged as a promising technique, and variable selection demonstrated a greater impact on modeling than CV variants.

Conclusions:

  • The choice of cross-validation protocol and modeling technique significantly affects prediction performance.
  • SVM, particularly with variable selection, offers superior predictive power compared to other tested methods.
  • SRD provides a robust method for ranking the performance of different modeling and CV strategies.