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CliffRank framework improves activity-cliff ranking prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CliffRank, a novel dual-branch framework designed to improve the prediction of activity-cliff ranking in chemical compounds. This method combines absolute-activity regression with ranking-consistency learning, utilizing a thresholded listwise loss and Pairwise Preference Consistency (PPC) to better leverage available activity data. When tested on antimicrobial peptide and small-molecule datasets, CliffRank demonstrated strong performance, achieving high Spearman correlations and competitive Recall@50 scores, though optimal configurations varied across different datasets and model backbones. AI

IMPACT Introduces a novel framework for predicting chemical activity cliffs, potentially improving drug discovery and material science.

RANK_REASON The cluster contains a research paper detailing a new framework for activity-cliff ranking prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CliffRank framework improves activity-cliff ranking prediction

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The cluster contains a research paper detailing a new framework for activity-cliff ranking prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kewei Li, Rongying Zhang, Peiyu Yang, Zhongjian Wang, Qiuchen Zhao, Lan Huang, Fengfeng Zhou ·

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

    arXiv:2609.01673v1 Announce Type: cross 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 e…

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