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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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相关实验视频

Updated: Jan 11, 2026

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基于专家知识的药物重新定位增强图形神经网络

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    此摘要是机器生成的。

    本研究介绍了DReKGNN,这是一种用于药物重新定位的新框架,它通过大型语言模型 (LLM) 和图形神经网络 (GNN) 利用专家知识. DReKGNN通过整合生物机制来提高药物疾病关联预测,以获得更可解释和更准确的结果.

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    科学领域:

    • 计算生物学和化学信息学.
    • 药物的发现和开发.
    • 医疗保健中的人工智能.

    背景情况:

    • 药物重新定位加速了对现有药物的新治疗指示的识别.
    • 图形神经网络 (GNN) 对于建模药物疾病关联是有效的,但通常使用随机初始化的节点嵌入.
    • 现有的GNN方法缺乏可解释性,并未将生物数据库中的有价值的专家知识纳入.

    研究的目的:

    • 开发一个新的框架,DReKGNN,用于药物重新定位,将专家知识整合到GNN中.
    • 提高用于药物疾病关联预测的节点嵌入的可解释性和准确性.
    • 提高药物发现管道的效率和有效性.

    主要方法:

    • DReKGNN利用大型语言模型 (LLM) 作为一个语义桥梁,将DrugBank和OMIM数据库的专家知识纳入其中.
    • 专家知识描述,专注于生物机制,直接从数据库中提取,避免提示模板.
    • 通过LLM生成的节点嵌入与GNN集成,使用平均聚合策略来减轻噪音并改善预测.

    主要成果:

    • 实验结果表明,与现有方法相比,DReKGNN在预测药物疾病关联方面表现优越.
    • 案例研究进一步证实了DReKGNN框架的有效性.
    • 生成的节点嵌入式是可解释的,并与专家生物知识保持一致.

    结论:

    • 通过LLM和GNN整合专家知识,DReKGNN有效地提高了药物重新定位.
    • 该框架提供可解释的节点嵌入,推进AI驱动药物发现领域.
    • DReKGNN提供了一种有前途的方法来加快新药指示的识别.