摘要: 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 in vitro 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 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 Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical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.
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来自:
李叙潼
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分类:
药物科学
>>
药物设计
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说明:
正在评审中,此处投稿为初稿,未经审稿人修稿
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投稿状态:
正在评审中
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引用:
ChinaXiv:202402.00168
(或此版本
ChinaXiv:202402.00168V1)
DOI:10.12074/202402.00168V1
CSTR:32003.36.ChinaXiv.202402.00168.V1
- 推荐引用方式:
Jin Liu,Yike Gui,Jingxin Rao,Jingjing Sun,Gang Wang,Qun Ren,Ning Qu,Buying Niu,Zhiyi Chen,Xia Sheng,Yitian Wang,Mingyue Zheng,Xutong Li.(2024).In Silico Off-Target Profiling for Enhanced Drug Safety Assessment.中国科学院科技论文预发布平台.doi:10.12074/202402.00168V1
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