概括
一个新的防泄漏数据集 (LP-PDBBind) 通过防止数据污染,改善了蛋白质 - 配体结合的预测. 使用此数据集重新训练的评分函数 (SF) 显示了增强的性能,特别是在新型复合体中.
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:
- 蛋白质 - 配体结合的自由能量预测对于药物发现至关重要.
- 在PDBBind上训练的现有评分功能 (SF) 可能因数据泄露而过度配置.
- 在PDBBind数据集中的交叉污染限制了SFs对新蛋白质-连接体复合物的概括性.
结论:
- LP-PDBBind数据集为评估和开发新的SF提供了可靠的基准.
- 解决数据泄露对于提高计算药物设计工具的准确性至关重要.
- 这些发现支持使用IGN来更好地预测蛋白质-连接体相互作用.
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