MARS: A neurosymbolic approach for interpretable drug discovery

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 mechanismof-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. Drug discovery (DD), the search for novel drugs or chemical compounds to treat ailments, often involves the screening of thousands of small compounds (Lin et al., 2020). Many computational approaches have been developed to accelerate and streamline this screening process (Gottlieb et al., 2011; Gan et al., 2023). Specifically, hundreds of such approaches operate upon knowledge graphs (KGs), in which nodes representing drugs, proteins, or medical conditions are connected by edges, representing the relationships between them (Chen et al., 2020).