Benchmarks for interpretation of QSAR models

Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is parti...

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Veröffentlicht in:	Journal of cheminformatics Jg. 13; H. 1; S. 41 - 20
Hauptverfasser:	Matveieva, Mariia, Polishchuk, Pavel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 26.05.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Artificial neural networks Atom contributions Benchmark data set Benchmarks Biological activity Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Datasets Decision making Documentation and Information in Chemistry Graph convolutional neural networks Interpretability metrics Neural networks Optimization Performance evaluation Prediction models QSAR model interpretation Research Article Structure-activity relationships Synthetic data set Task complexity Theoretical and Computational Chemistry Synthetic data set QSAR model interpretation Interpretability metrics Benchmark data set Graph convolutional neural networks Atom contributions
ISSN:	1758-2946, 1758-2946
Online-Zugang:	Volltext
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