CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction

2026-09-01

AI Summary

为解决因局部结构变化导致的活性差异问题，本文提出CliffRank框架，结合绝对活性回归与排序一致性学习方法，在多个数据集上验证了其有效性。

Abstract

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.

Problem

• Activity-cliff ranking
• Local structural changes
• Activity differences
• High-quality data

Innovation

• Dual-Branch Framework
• Activity-Cliff Ranking
• Pairwise Preference Consistency (PPC)
• Mean Squared Error
• Thresholded Listwise Loss