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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of cheminformatics Ročník 13; číslo 1; s. 41 - 20
Hlavní autoři:	Matveieva, Mariia, Polishchuk, Pavel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 26.05.2021 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!