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Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.<\/jats:p>","DOI":"10.1021\/jacs.6c08189","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T15:02:22Z","timestamp":1786201342000},"page":"34442-34455","source":"Crossref","is-referenced-by-count":1,"title":["An Explicit Interaction-Prompted\nDiffusion Framework\nfor High-Fidelity 3D Molecular Generation"],"prefix":"10.1021","volume":"148","author":[{"given":"Huabin","family":"Du","sequence":"first","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University , , ,","place":["Hangzhou, People\u2019s Republic of China, 310058"]},{"name":"Zhejiang Provincial People\u2019s Hospital , ,","place":["Hangzhou, People\u2019s Republic of China, 310014"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences , , ,","place":["Hangzhou, People\u2019s Republic of China, 310024"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengke","family":"Yang","sequence":"additional","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchao","family":"Yan","sequence":"additional","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"An","sequence":"additional","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7971-5401","authenticated-orcid":true,"given":"Arieh","family":"Warshel","sequence":"additional","affiliation":[{"name":"University of Southern California Los Angeles , , , ,","place":["Los Angeles, California, United States, 90089-1062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-2580","authenticated-orcid":true,"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[{"name":"Zhejiang University , , ,","place":["Hangzhou, People\u2019s Republic of China, 310058"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4560-3019","authenticated-orcid":true,"given":"Chen","family":"Bai","sequence":"additional","affiliation":[{"name":"Chenzhu (MoMeD) Biotechnology Co., Ltd. , ,","place":["Hangzhou, People\u2019s Republic of China, 311100"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"2026081910241366400_cit1","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning\nfor image recognition","author":"He","year":"2016","journal-title":"Proc. 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