BOLD-GPCRs:用于预测甲基生物活性和A类GPCR中的突变效应的变压器驱动应用程序
Davide Provasi1, Kirill Konovalov1, Nicholas Riina2
1Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Journal of chemical information and modeling
|January 14, 2026
概括
BOLD-GPCRs是一种深度学习框架,通过准确预测G蛋白结合受体 (GPCR) 配体的生物活性来增强药物发现. 这个工具有助于识别未经研究的GPCRs的治疗方法.
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 (GPCR) 是许多疾病中涉及的关键药物标.
- 传统的药物发现方法与缺乏广泛的配体或结构数据的GPCRs作斗争.
- 需要先进的策略来准确地预测整个GPCR家族的配体生物活性,特别是对研究不足的亚型.
研究的目的:
- 引入BOLD-GPCRs,这是一个深度学习框架,用于在A类GPCR中增强联体生物活性预测.
- 为访问BOLD-GPCRs框架提供一个用户友好的Web界面.
- 利用转移学习和精心策划的数据集来改善GPCR药物发现.
主要方法:
- 通过使用深度学习开发了BOLD-GPCRs (BERT优化GPCRs的基发现).
- 集成密集神经网络分类器与基于变压器的蛋白质语言模型.
- 使用了A类GPCR配体,受体序列和突变的精选数据集.
主要成果:
- BOLD-GPCRs在A类GPCR中表现出对联体生物活性的强大预测性能.
- 该框架准确地预测受体突变对连接体活性的影响.
- 取得了强有力的结果,特别是对于表征不佳的GPCR亚型.
结论:
- BOLD-GPCRs是增强GPCR连体发现的宝贵工具.
- 该框架显示了识别针对研究不足的GPCRs的治疗方法的巨大潜力.
- 深度学习方法为GPCR向药物开发提供了先进的解决方案.
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