AI 中文总结
本研究提出参数高效测试时自适应框架PETA，仅更新预训练虚拟筛选模型约0.03%的LayerNorm参数，在测试时通过构建特定负样本等方式自适应，性能优于预训练及全重训练基线。
AI 中文摘要
从海量化学文库中准确排序靶蛋白口袋的活性配体，仍是虚拟筛选的核心挑战。DrugCLIP及其近期扩展模型通过将蛋白口袋和分子编码到共享嵌入空间，大幅加速了这一过程。尽管取得了这些进展，但进一步的性能提升通常需要重新训练整个模型，产生大量计算开销，且使靶标特定定制效率低下。在本研究中，我们将预训练虚拟筛选模型对单个口袋的定制化为测试时自适应问题，并提出PETA——一个参数高效的框架，可在测试时直接自适应预训练模型。给定一个靶蛋白口袋，PETA通过分子扩散和化学有效性过滤构建口袋特定的负样本，进一步通过嵌入空间混合将其移至从结构数据库检索的参考配体附近，以创建更具挑战性的排序任务。随后，排序目标更加强调抑制高分无效候选，这些候选可能污染排名靠前的筛选结果，为轻量自适应提供结构化监督。在不同基准上的实验表明，这种轻量、口袋特定的自适应方法，仅更新约占整个模型0.03%的LayerNorm参数，其性能优于预训练基线和完全重新训练的基线。
英文摘要
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.
