Author

Y. Ong

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Preprint Aug 2026

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

This work forms the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and proposes PETA, a parameter-efficient framework that directly adapts pretrained model at test time and outperforms both pretrained and fully retrained baselines while updating only the LayerNorm parameters.

Jia-Qi Lin, Yinghua Yao, Changran Wang et al. · 0 citations