CQFiner: Protein Context Query Refinement for Test-Time Scaling Resolution of Binding Site Detection

Protein binding site prediction is one of the most fundamental problems in drug discovery and structural biology. A mainstream approach is to place anchors or query nodes at different locations, move and classify these queries, and finally use the coordinates of high-confidence queries as the predicted binding site locations. Resolution is a key determinant of predictive performance of such methods. However, in this work we demonstrate that the available resolution upper limit is constrained by GPU memory, over-smoothing, and suboptimal training dynamics. More importantly, these query-based methods fix the query number and distribution during training and inference, thus cannot benefit from higher spatial resolution at inference, or specific query sampling strategy according to user priors. Here we introduce Context Query Refiner (CQFiner). CQFiner models the protein as a fixed readonly context and formulates binding site prediction as an object detection task conditioned on this context, refining both query positions and confidence scores from coarse to fine. We demonstrate how object detection practices help the model benefit from higher resolution and how readonly protein context enables test time scaling and prior-based query sampling. Based on the architecture, CQFiner substantially outperforms state-of-the-art methods on both in-distribution and out-of-distribution benchmarks, such as COACH420, HOLO4K, etc.