CliffRank：用于活性悬崖排名预测的双分支框架

中文摘要 AI 辅助

活性悬崖排名预测仍存在困难，因为局部结构变化可导致活性出现巨大差异，而能阐明其潜在机制的高质量数据仍然有限。为更有效地利用现有活性标签，我们将绝对活性回归与排名一致性学习相结合。CliffRank训练两个并行预测器，采用均方误差、阈值化列表损失和成对偏好一致性（Pairwise Preference Consistency，PPC），该方法可在偏好概率空间中对齐相对排序。在三个抗菌肽数据集上，采用ESM2-t12的CliffRank达到了最高的平均斯皮尔曼相关系数（0.5393）和平均Recall@50（21.4），尽管各数据集上的领先方法有所不同。在三个小分子数据集上，采用PNA的CliffRank（PPC在120个epoch后激活）达到了最高的平均斯皮尔曼相关系数（0.6890），其平均Recall@50为30.4，与ACANet-PNA的表现相当。PPC的结果也明确了其实际应用局限。非对称初始化提升了MolCLR-GIN的平均表现，但并非对所有目标均有改善。对于未使用预训练权重的PNA，延迟应用PPC可改善部分指标，但不存在一种调度方案能同时优化平均斯皮尔曼相关系数和平均Recall@50。未来研究应评估更多目标和抗菌肽系统，开发自适应PPC调度方案，并在可用时纳入蛋白质或膜上下文信息。

英文摘要

Activity-cliff ranking remains difficult because local structural changes can cause large activity differences, while high-quality data that resolve the underlying mechanisms remain limited. To use available activity labels more effectively, we combine absolute-activity regression with ranking-consistency learning. CliffRank trains two parallel predictors with mean squared error, a thresholded listwise loss, and Pairwise Preference Consistency (PPC), which aligns relative ordering in the preference-probability space. On three antimicrobial peptide datasets, CliffRank with ESM2-t12 achieved the highest mean Spearman correlation of 0.5393 and mean Recall@50 of 21.4, although the leading method varied across individual datasets. On three small-molecule datasets, CliffRank with PNA, where PPC was activated after 120 epochs, achieved the highest mean Spearman correlation of 0.6890, while its mean Recall@50 of 30.4 matched that of ACANet-PNA. The PPC results also define its practical limits. Asymmetric initialization improved the MolCLR-GIN averages but did not improve every target. For PNA without pretrained weights, delayed PPC improved selected metrics, but no schedule was best for both mean Spearman correlation and mean Recall@50. Future work should evaluate more targets and antimicrobial peptide systems, develop adaptive PPC schedules, and incorporate protein or membrane context when available.

发表机构

• Jilin University（吉林大学）
• Greenwich High School（格林威治高中）
• BCPM Data Limited（BCPM数据有限公司）
• University of Cambridge（剑桥大学）

机构由 AI 辅助整理，请以论文原文为准。