微调的深度转移学习模型用于对更安全的药物进行大规模查,目标是A类GPCRs
bioRxiv : the preprint server for biology
|December 23, 2024
概括
研究人员开发了人工智能模型来预测更安全的G蛋白结合受体 (GPCR) 药物. 这些模型可以识别具有较低内在功效或连接物偏差的化合物,从而有可能提高药物安全性和药物开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- G蛋白结合受体 (GPCRs) 对于细胞信号传递至关重要,是主要的药物标.
- 将药物的疗效与特定受体激活和生理结果联系起来是具有挑战性的.
- 了解药物安全性 (治疗窗口) 是否来自低疗效或带偏差是关键.
研究的目的:
- 开发人工智能模型来预测具有更好的安全配置文件的GPCR配体.
- 克服高质量数据的局限性,用于GPCR药物发现中的深度学习.
- 为了能够准确地预测低效率和偏差的GPCR激动剂.
主要方法:
- 预先训练了A类GPCR序列和配体数据的深度学习模型.
- 利用转移学习和使用NLP的神经网络用于目标序列.
- 嵌入式受体突变对微调模型的信号传输的影响.
- 开发了低效率和偏倚激动剂的单独模型.
主要成果:
- 创建了两种微调的人工智能模型,用于预测低效率和有偏见的GPCR激活剂.
- 单个A类GPCR的模型可按需提供.
- 启用了大型化学库的虚拟选,以寻找更安全的候选药物.
结论:
- 人工智能,特别是转移学习和NLP,可以预测具有所需生物活性的GPCR配体.
- 这些模型有助于发现具有潜在增强安全概况的化合物.
- 通过人工智能驱动的有效性和偏差预测来推进GPCR药物开发.
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