Knowledge graph aids comprehensive explanation of drug and chemical toxicity

Abstract

In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.

FIGURE 1

Incorporating curated chemical‐gene connections into VNN for toxicity prediction with AIDTox. (a) Three types of chemical‐gene connections (binding, expression, and hybrid) are extracted from ComptoxAI to construct the input feature profile of AIDTox. Feature selection is implemented to identify the top gene features predictive of the outcome of interest. The selected profile is fed into a VNN, whose structure is guided by Reactome pathway hierarchy. Specific pathways and general processes are coded as modules by hidden neurons. Barplots showing the comparison of validation performance across three connection types in two cell viability datasets: HEK293 (b) and HepG2 (c). Performance is measured by three metrics: area under ROC curve, balanced accuracy, and F1 score, with error bar showing the 95% confidence interval. Barplots showing the comparison of validation performance across four models in two cell viability datasets: HEK293 (d) and HepG2 (e). Three other models are considered: (i) our previous DTox model with inferred target profile as input, (ii) QSAR model by random forest with chemical fingerprint as input, and (iii) QSAR model by gradient boosting with chemical fingerprint as input. For area under ROC curve and balanced accuracy scores, the “no predicative value” level is 0.5. QSAR, quantitative structure–activity relationship; ROC, receiver operating characteristic; VNN, visible neural network.

FIGURE 2

Comprehensive explanation of HEK293 cytotoxicity with new features in AIDTox. (a) Radar plot showing the comparison of gene category distributions among the features of DTox (blue solid line) and AIDTox (red solid line). The gray dashed line shows the hypothetical of a proportionate increase from DTox to AIDTox. (b) Radar plot showing the comparison of enzyme subcategory distributions among the features of DTox (blue solid line) and AIDTox (red solid line). (c) Sankey diagram showing the AIDTox explanation of HEK293 cytotoxicity for drugs targeting tubulin proteins. The paths (connecting drugs to HEK293 cell death) shown in the diagram are identified from the full network of VNN model by the AIDTox interpretation framework. Connections in the VNN are informed by ComptoxAI (chemical‐gene) and Reactome (gene‐pathway and child–parent pathway). Tubulin proteins are grouped and colored by the family. (d) Sankey diagram showing the AIDTox explanation of HEK293 cytotoxicity via ATP‐binding cassette transporters (similar to c). Drugs are grouped and colored by the ATC subclass (first three digits of the ATC code). (e) Sankey diagram showing the AIDTox explanation of HEK293 cytotoxicity via cytochrome P450 enzymes (similar to c). Drugs are grouped and colored by the ATC subclass (first three digits of the ATC code). Cytochrome P450 enzymes are grouped and colored by the family. Metabolic pathways are grouped and colored by the general metabolic process they belong to (“Biological oxidations” or “Metabolism of lipids”). ADME, absorption, distribution, metabolism, and excretion; ATC, Anatomic Therapeutic Chemical.