Keep looking at the negative side: improved detection of drug-induced liver injury with non-hepatotoxicant data oversampling

Drug-induced liver injury (DILI) presents a critical challenge in drug development, often leading to the withdrawal of promising therapeutic candidates. Traditional predictive models for DILI, typically relying on molecular descriptors and pharmacokinetic properties, are insufficient due to the complex and multifactorial nature of liver toxicity. This complexity stems from overlapping biological stress responses activated by both hepatotoxic and non-hepatotoxic compounds, making it difficult to distinguish between them accurately. Additionally, the scarcity of DILI-positive compounds in available datasets results in significant class imbalance, further limiting the efficacy of conventional predictive models. Addressing these challenges requires novel approaches incorporating molecular and bioactivity data to enhance predictive power. In this study, we developed a custom oversampling strategy tailored to handle DILI's biological complexity and class imbalance. We integrated stress pathway activations, particularly focusing on the oxidative, unfolded protein, DNA damage, heat shock, and cytokine signalling stress responses, with molecular descriptors and bioactivity profiles to improve model performance. The custom oversampling technique demonstrated improved specificity and overall predictive accuracy, mitigating the effects of class imbalance without overfitting. Despite these advances, significant challenges remain in refining predictive models, particularly in identifying the most informative biological markers and optimising experimental protocols for better data acquisition. Our results suggest that while incorporating diverse data types and novel oversampling strategies improves DILI prediction, further efforts are required to create robust, generalisable models capable of reliably predicting hepatotoxicity in the drug development process.