Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug-gene-adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene-ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert-gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

中文翻译：

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通过学习型复杂网络进行药物安全的广谱分析。

药物安全性是一个严重的临床药理和毒理学问题，每年都会造成巨大的医疗和社会负担。遗憾的是，仍然缺少可重复使用的系统地和定量地评估药物安全性的方法。在这项研究中，我们通过解决多层药物-基因-药物不良反应（ADR）相互作用网络，开发了用于从头药物安全性评估的高级机器学习模型。首次在1156种不同的ADR的广泛范围内评估了药物安全性。我们还设计了一个参数ToxicityScore来量化整体药物安全性。此外，我们确定了每3,807,631个基因与ADR相互作用的关联强度，这为ADR的机理探索提供了线索。为了方便起见，我们在http://www.bio-add.org/ADRAlert/上将该模型作为Web服务ADRAlert-gene进行了部署。总之，这项研究为优先考虑安全药物治疗提供了见识。通过在临床前早期提供可靠的ADR资料，它有助于降低新药发现的损耗率。