Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug–drug interactions. While there are many computational studies that explore models to predict ADRs originating from single drugs, only a few of them explore models that predict ADRs from drug combinations. Further, as far as we know, none of them have developed models using transcriptomic data, specifically the LINCS L1000 drug‐induced gene expression data to predict ADRs for drug combinations. In this study, we use the TWOSIDES database as a source of ADRs originating from two‐drug combinations. 34,549 common drug pairs between these two databases were used to train an artificial neural network (ANN), to predict 243 ADRs that were induced by at least 10% of the drug pairs. Our model predicts the occurrence of these ADRs with an average accuracy of 82% across a multifold cross‐validation.

中文翻译：

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使用结构和转录组药物表征来训练人工神经网络来预测两种药物组合的药物不良反应

药物不良反应 (ADR) 是由药物与各种来源（包括药物 - 药物相互作用）的相互作用引发的药理学事件。虽然有许多计算研究探索了预测源自单一药物的 ADR 的模型，但只有少数研究探索了预测来自药物组合的 ADR 的模型。此外，据我们所知，他们都没有开发出使用转录组数据的模型，特别是 LINCS L1000 药物诱导的基因表达数据来预测药物组合的 ADR。在本研究中，我们使用 TWOSIDES 数据库作为源自两种药物组合的 ADR 来源。这两个数据库之间的 34,549 个常见药物对用于训练人工神经网络 (ANN)，以预测至少 10% 的药物对诱发的 243 个 ADR。