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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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
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Popis
Shrnutí:	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 particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypothesis. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks, interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex “black box” models.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1758-2946 1758-2946
DOI:	10.1186/s13321-021-00519-x