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Although deep learning (DL) models have shown strong performance, the potential of traditional statistical potentials under data-limited conditions remains underexplored. Here, we systematically assess several statistical potential models in docking and virtual screening. We find that docking benefits from distance-dependent pairwise atom\u2013atom potentials with clear physical meanings, while screening relies more on orientation-dependent atom\u2013residue potentials that capture local chemical environments. Based on these findings, we propose HybridSP, a hybrid potential combining distance-dependent atom\u2013atom, atom\u2013residue, and orientation-dependent atom\u2013residue terms. An affinity-weighted scheme is applied to correct biases in statistical distributions. On the CASF-2016 benchmark, HybridSP achieves a 91.6% docking success rate and an enrichment factor of 29.35 at the top 1%, rivaling and even surpassing state-of-the-art DL models. Its strong screening ability is further validated on directory of useful decoys-enhanced and directory of useful decoys-adjusted. These results demonstrate that well-designed statistical potentials can achieve high performance and interpretability without complex DL architectures, offering an efficient alternative for scoring function design. The models are available at: https:\/\/github.com\/zelixirSH\/HybridSP.git.<\/jats:p>","DOI":"10.1093\/bib\/bbag088","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:44:09Z","timestamp":1770813849000},"source":"Crossref","is-referenced-by-count":2,"title":["Could statistical potential models achieve comparable or better performance than deep learning models?"],"prefix":"10.1093","volume":"27","author":[{"given":"Zhihao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Physics, Shandong University , 27 Shanda Nan Road, 250100 Jinan, Shandong Province ,","place":["China"]}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Zelixir Biotech Co. Ltd. , 298 Xiangke Road, 201210 Shanghai ,","place":["China"]}]},{"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery , Faculty of Applied Science, Macao Polytechnic University, R. de Lu\u00eds Gonzaga Gomes, 999078 Macao,","place":["China"]}]},{"given":"Yuguang","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Nanyang Technological University , 50 Nanyang Avenue, 639798 ,","place":["Singapore"]}]},{"given":"Xiangdong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Physics, Shandong University , 27 Shanda Nan Road, 250100 Jinan, Shandong Province ,","place":["China"]}]},{"given":"Liangzhen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanghai Zelixir Biotech Co. 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