PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

2026-08-20

AI Summary

本文提出PETA，一种参数高效的测试时适应框架，通过构建特定口袋负样本和排名目标优化，解决虚拟筛选中活性配体准确排序问题。

Abstract

Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP and its recent extensions substantially accelerate this process by encoding protein pockets and molecules into a shared embedding space. Despite this progress, further performance improvements typically require retraining the entire model, incurring substantial computational overhead and making target-specific customization inefficient. In this work, we formulate the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and propose PETA, a parameter-efficient framework that directly adapts pretrained model at test time. Given a target pocket, PETA constructs pocket-specific negatives through molecular diffusion and chemical validity filtering, and further moves them toward the reference ligand retrieved from structural databases via embedding-space mixup to create more challenging ranking tasks. A ranking objective then places greater emphasis on suppressing high-scoring invalid candidates that could contaminate the top-ranked screening results, providing structured supervision for lightweight adaptation. Experiments across diverse benchmarks demonstrate that this lightweight, pocket-specific adaptation outperforms both pretrained and fully retrained baselines while updating only the LayerNorm parameters, which account for approximately $0.03\%$ of the full model.

Problem

• virtual screening
• active ligands
• protein pocket
• pretrained model
• test-time adaptation

Innovation

• Parameter-Efficient
• Test-Time Adaptation
• Virtual Screening
• Pocket-Specific Negatives
• Embedding-Space Mixup