GatorAffinity:通过大规模的合成结构数据来提高蛋白质-连接物结合的亲和力预测
Jinhang Wei1, Yupu Zhang2, Peter A Ramdhan1
1Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, USA.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
这项研究解决了药物发现中的数据短缺问题,通过使用合成蛋白质 - 连接体复合体来训练深度学习模型GatorAffinity. 该模型显著提高了蛋白质 - 配体结合亲和力预测的准确性和概括性.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 预测蛋白质-连接体结合亲和力对于药物发现至关重要,但由于稀缺的实验数据而受到限制.
- 像PDBbind这样的现有数据集不足以训练可靠的数据驱动模型.
- 由于缺乏结构信息,大量的亲和数据未得到充分利用.
研究的目的:
- 为了克服蛋白质 - 配体结合亲缘关系预测的数据稀缺性.
- 开发一个高度准确和可概括的深度学习模型用于亲和预测.
- 为了利用大规模的合成蛋白质 - 配体复合物的数据.
主要方法:
- 使用博尔茨-1模型,策划了超过45万个合成蛋白质-连接体复合体,具有Kd和Ki值.
- 来自SAIR数据库 (IC50值) 的超过100万个合成复合物的增强数据.
- 开发了GatorAffinity,一个几何深度学习评分功能,在合成数据上进行预训练,并在PDBbind实验数据上进行微调.
主要成果:
- 在一个防泄漏的基准上,GatorAffinity显著超过了最先进的亲和力预测方法.
- 与现有方法相比,证明了更高的准确性和通用性.
- 验证了合成数据增强有效地解决了数据稀缺问题,同时保持了预测可靠性.
结论:
- 用大规模合成复合物增强实验数据是提高亲和预测的可行策略.
- GatorAffinity为虚拟查和基于结构的药物设计提供了一个可扩展和可重复的基础.
- 预训练的GatorAffinity模型和GatorAffinity-DB数据集已发布,以促进进一步的研究.
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