Benchmarks for interpretation of QSAR models

Journal of Cheminformatics

Pavel Polishchuk¹  Mariia Matveieva¹ 

[1] Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic;

关键词: QSAR model interpretation;    Benchmark data set;    Synthetic data set;    Interpretability metrics;    Atom contributions;    Graph convolutional neural networks;   

DOI  :  10.1186/s13321-021-00519-x

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY