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

• URL: http://arxiv.org/abs/2608.19906v1
• Date: Thu, 20 Aug 2026 11:17:27 GMT
• Title: PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening
• Abstract summary: We propose PETA, a parameter-efficient framework that directly adapts pretrained model at test time.<n>PETA constructs pocket-specific negatives through molecular diffusion and chemical validity filtering.<n>A ranking objective places greater emphasis on suppressing high-scoring invalid candidates that could contaminate the top-ranked screening results.
• Score: 49.976632553053285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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