🤖 AI Summary
This study addresses the interpretability and biological plausibility of neuro-symbolic AI for mechanism-of-action (MoA) deconvolution in drug discovery, specifically tackling reasoning shortcuts and prediction biases induced by degree bias in knowledge graph models. To this end, we propose (i) the first dedicated evaluation paradigm for MoA deconvolution; (ii) MoA-net, a structured prior knowledge graph encoding domain-specific pharmacological relationships; and (iii) MARS, a neuro-symbolic system that jointly learns logical rules and graph embeddings for end-to-end, biologically grounded inference. We explicitly mitigate degree bias through diagnostic analysis and targeted regularization. Experiments demonstrate that MARS achieves state-of-the-art performance on MoA prediction while generating inference paths that align closely with established pharmacological mechanisms. This significantly enhances model transparency, scientific credibility, and practical utility in biomedical discovery.
📝 Abstract
Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretability is broadly defined, there are no clear guidelines for assessing the biological plausibility of model interpretations. To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowledge graph (KG), MoA-net. We then develop the MoA Retrieval System (MARS), a NeSy approach for drug discovery which leverages logical rules with learned rule weights. Using this interpretable feature alongside domain knowledge, we find that MARS and other NeSy approaches on KGs are susceptible to reasoning shortcuts, in which the prediction of true labels is driven by"degree-bias"rather than the domain-based rules. Subsequently, we demonstrate ways to identify and mitigate this. Thereafter, MARS achieves performance on par with current state-of-the-art models while producing model interpretations aligned with known MoAs.