释放AlphaFold3类方法在虚拟选中的应用潜力
Chao Shen1,2,3, Xujun Zhang2,4, Shukai Gu2,4
1Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine Hangzhou Zhejiang 310003 China shenchao513@zju.edu.cn tingjunhou@zju.edu.cn.
Chemical science
|December 17, 2025
概括
AlphaFold3 (AF3) 显示了基于结构的虚拟选 (VS) 的强大潜力,使用其内部信心指标来排名化合物. 虽然有效,但复杂的数据集和有限的训练数据会带来性能挑战,但它往往优于传统的对接方法.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白质连接体复杂结构预测已经被AlphaFold3 (AF3) 彻底改变.
- 基于结构的虚拟查 (VS) 的AF3类型模型的应用是一个需要系统评估的新兴领域.
- 现有的虚拟选方法往往在准确性和效率方面面临限制.
研究的目的:
- 系统地评估基于结构的虚拟选的AlphaFold3类方法的有效性.
- 为了比较AF3,Protenix和Boltz-2在虚拟选任务中的性能.
- 在药物发现管道中识别这些先进模型的优点和局限性.
主要方法:
- 使用AlphaFold3,Protenix和Boltz-2作为虚拟选的代表模型.
- 在已建立的数据集 (如DEKOIS2.0) 和具有挑战性的自定义数据集上进行基准性能.
- 评估的内在信心指标和第三方评分方案,用于复合排名.
- 评估预测结构的姿势生成准确性和形状合理性.
主要成果:
- AlphaFold3表现出了卓越的选能力,主要是由于其内在的信心指标.
- 在大多数场景中,AF3和Protenix都充当了强大的姿势生成器,在大多数场景中都超过了传统的对接工具.
- 在具有挑战性的病例中,性能下降,包括化学上相似的活性物质排除,新型GPCR数据集和经过实验验证的非活性化合物.
- 预测的姿势通常采用物理上可信的形状与小的结构文物.
结论:
- 与AF3类似的方法显示出在药物发现中基于结构的虚拟查的重大前景.
- 内在的信心指标是AF3查表现的关键驱动因素.
- 目前的局限性需要仔细考虑在复杂的药物发现场景中部署.
- 这些模型提供了有价值的见解,并经常超过传统的对接精度,尽管目前的限制.
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