Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

中文翻译：

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计算机脱靶分析以增强药物安全性评估

确保药物开发早期阶段的药物安全对于避免后续阶段代价高昂的失败至关重要。然而，通过安全筛选和动物试验检测药物脱靶和潜在副作用所带来的经济负担是巨大的。药物脱靶相互作用及其引起的药物不良反应是影响药物安全性的重要因素。为了评估候选药物的责任，我们开发了一个人工智能模型，利用多任务图神经网络来精确预测化合物脱靶相互作用。脱靶预测的结果可以作为化合物的表示，从而能够区分各种 ATC 代码下的药物以及化合物毒性的分类。此外，预测的脱靶概况用于药物不良反应（ADR）富集分析，有助于推断药物的潜在 ADR。以已撤回的药物Pergolide为例，我们在靶标水平上阐明了ADR的潜在机制，有助于探索新预测的脱靶相互作用的潜在临床相关性。总体而言，我们的工作有助于基于脱靶鉴定的化合物安全性/毒性的早期评估，推断药物潜在的ADR，并最终促进药物的安全开发。