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.

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QSAR 模型解释的基准

QSAR 模型的解释有助于理解生物或物理化学过程的复杂性质、指导结构优化或对 QSAR 模型进行基于知识的验证。高度预测的模型通常很复杂，并且它们的解释也很重要。对于现代神经网络来说尤其如此。存在多种解释这些模型的方法。然而，很难评估和比较这些不断出现的方法的性能和适用性。在这里，我们开发了几个基准数据集，其终点由预定义的模式确定。这些数据集旨在评估解释方法检索这些模式的能力。它们代表具有不同复杂程度的任务：从简单的基于原子的加性特性到药效团假设。我们提出了几种解释性能的定量指标。基准和指标的适用性在一组传统模型和端到端图卷积神经网络上得到了证明，并由之前建议的通用 ML 不可知结构解释方法进行解释。我们预计这些基准将有助于评估新的解释方法和研究复杂“黑匣子”模型的决策。