Literature DB >> 30779563

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Robert P Sheridan1.   

Abstract

Most chemists would agree that the ability to interpret a quantitative structure-activity relationship (QSAR) model is as important as the ability of the model to make accurate predictions. One type of interpretation is coloration of atoms in molecules according to the contribution of each atom to the predicted activity, as in "heat maps". The ability to determine which parts of a molecule increase the activity in question and which decrease it should be useful to chemists who want to modify the molecule. For that type of application, we would hope the coloration to not be particularly sensitive to the details of model building. In this Article, we examine a number of aspects of coloration against 20 combinations of descriptors and QSAR methods. We demonstrate that atom-level coloration is much less robust to descriptor/method combinations than cross-validated predictions. Even in ideal cases where the contribution of individual atoms is known, we cannot always recover the important atoms for some descriptor/method combinations. Thus, model interpretation by atom coloration may not be as simple as it first appeared.

Mesh:

Year:  2019        PMID: 30779563     DOI: 10.1021/acs.jcim.8b00825

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  6 in total

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2.  Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.

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Journal:  Front Bioinform       Date:  2022-06-17

3.  Benchmarks for interpretation of QSAR models.

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Journal:  J Cheminform       Date:  2021-05-26       Impact factor: 5.514

4.  Model agnostic generation of counterfactual explanations for molecules.

Authors:  Geemi P Wellawatte; Aditi Seshadri; Andrew D White
Journal:  Chem Sci       Date:  2022-02-16       Impact factor: 9.825

5.  Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.

Authors:  Jeffrey K Weber; Joseph A Morrone; Sugato Bagchi; Jan D Estrada Pabon; Seung-Gu Kang; Leili Zhang; Wendy D Cornell
Journal:  J Comput Aided Mol Des       Date:  2021-11-24       Impact factor: 4.179

6.  Revealing cytotoxic substructures in molecules using deep learning.

Authors:  Henry E Webel; Talia B Kimber; Silke Radetzki; Martin Neuenschwander; Marc Nazaré; Andrea Volkamer
Journal:  J Comput Aided Mol Des       Date:  2020-04-16       Impact factor: 3.686

  6 in total

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