生物活性 深度学习用于复杂的无结构化合物-蛋白质相互作用预测
Yaowen Gu1, Song Xia1, Qi Ouyang1
1Department of Chemistry, New York University, New York, New York 10003, United States.
Journal of chemical information and modeling
|September 16, 2025
概括
我们介绍了CPI2M,一个大型生物活性数据集,以及GGAP-CPI,一个深度学习模型. GGAP-CPI有效地预测化合物-蛋白相互作用 (CPI) 和处理活动悬崖 (ACs),优于药物选中的现有方法.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 药物发现和开发 药物发现和开发
背景情况:
- 准确的蛋白质 - 配体结合亲和力预测对于虚拟药物查至关重要.
- 传统的方法依赖于有限的蛋白质-连接体晶体结构.
- 无结构化合物-蛋白相互作用 (CPI) 方法提供使用生物活性数据的替代方案,但面临数据异质性和活性悬崖 (AC) 的挑战.
研究的目的:
- 解决无结构CPI预测的局限性,特别是数据异质性和ACs.
- 引入一个具有AC注释的大规模基准数据集 (CPI2M).
- 开发和验证一种新的深度学习模型 (GGAP-CPI) 以进行可靠的CPI预测.
主要方法:
- 创建CPI2M,一个数据集,包含四种活动类型 (Ki,Kd,EC50,IC50) 的约200万个生物活性点和AC注释.
- 开发GGAP-CPI,这是一个无结构的深度学习模型,利用集成的生物活性学习和高级蛋白质表示.
- 对GGAP-CPI进行综合评估,与4种预测场景和7种基准数据集中的19种基线方法对比.
主要成果:
- GGAP-CPI显著超过了12个特定目标和7个一般CPI基线.
- 该模型在一般CPI预测,罕见蛋白质预测,转移学习和虚拟查方面表现出卓越的表现.
- GGAP-CPI提供稳定的生物活性预测,测量预测不确定性,并识别绑定口袋残留物和相互作用.
结论:
- GGAP-CPI代表了无结构CPI预测的重大进步,有效地解决了数据异质性和活动悬崖.
- 该模型能够预测生物活性,量化不确定性和丰富相互作用数据,这突显了其在药物发现中的实际实用性.
- CPI2M数据集和GGAP-CPI模型为推进计算药物查和生物活性评估提供了宝贵的资源.
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