{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:18:52Z","timestamp":1789395532216,"version":"build-2803163510"},"reference-count":38,"publisher":"American Chemical Society (ACS)","issue":"17","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3002300"],"award-info":[{"award-number":["2023YFB3002300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2024ZD0533500"],"award-info":[{"award-number":["2024ZD0533500"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 22373030"],"award-info":[{"award-number":["No. 22373030"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Structure-based molecular diffusion models have shown considerable potential for de novo drug design. However, their practical use in million-scale candidate-library construction remains limited by fixed target-agnostic inference settings, insufficient support for objective-aware molecular prioritization, and limited integration of scalable property evaluation with standardized library production. Here, we present DASH, a pocket-aware and objective-aware framework for converting protein-conditioned diffusion outputs into property-tagged molecular libraries for computational prioritization. DASH combines pocket-complexity-aware sampling, configurable objective-aware molecular scoring, and a scalable production layer. The sampling strategy adjusts inference effort according to geometric and physicochemical features of the target binding pocket. The Objective-Aware Quality Module (OQM) filters and reranks generated molecules using configurable descriptors, desirability functions, and scoring profiles. The production layer supports million-scale execution through asynchronous GPU\u2013CPU processing, streaming output, molecular scoring, and SDF annotation. We evaluated DASH through pocket-complexity analysis, OQM profile analysis, execution benchmarks, and multi-GPU\/multinode scaling experiments. The results show that DASH can adapt inference effort across pockets, rerank generated libraries under different design objectives, and produce standardized molecular libraries for downstream analysis. An EGFR-oriented case study further demonstrates how DASH-generated libraries can support downstream computational prioritization and representative candidate selection. Together, these results demonstrate that DASH extends protein-conditioned diffusion models from raw molecular generation toward practical-scale, objective-aware candidate-library construction and computational hit prioritization.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01357","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:23:35Z","timestamp":1787498615000},"page":"11024-11040","source":"Crossref","is-referenced-by-count":0,"title":["DASH: A Pocket-Aware and Objective-Aware Framework\nfor Million-Scale Structure-Based Molecular Generation"],"prefix":"10.1021","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6943-0485","authenticated-orcid":true,"given":"Baohua","family":"Zhang","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences , Dongsheng South Road , ,","place":["Beijing, China, 100083"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huangchao","family":"Xu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences , Dongsheng South Road , ,","place":["Beijing, China, 100083"]},{"name":"University of Chinese Academy of Sciences , , Yanqi Lake East Road , ,","place":["Beijing, China, 101408"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoning","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences , Dongsheng South Road , ,","place":["Beijing, China, 100083"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8435-4006","authenticated-orcid":true,"given":"Longfei","family":"Li","sequence":"additional","affiliation":[{"name":"Hebei University , , Wusi East Road , , ,","place":["Baoding, Hebei, China, 071002"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Jin","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences , Dongsheng South Road , ,","place":["Beijing, China, 100083"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"issue":"2","key":"2026091409072970600_cit1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/nrd.2017.232","article-title":"Automating\ndrug discovery","volume":"17","author":"Schneider","year":"2018","journal-title":"Nat. Rev. Drug Discovery"},{"issue":"7958","key":"2026091409072970600_cit2","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/s41586-023-05905-z","article-title":"Computational approaches streamlining drug discovery","volume":"616","author":"Sadybekov","year":"2023","journal-title":"Nature"},{"issue":"6","key":"2026091409072970600_cit3","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2024.104009","article-title":"How successful are AI-discovered drugs in clinical trials? a first\nanalysis and emerging lessons","volume":"29","author":"KP Jayatunga","year":"2024","journal-title":"Drug Discovery\ntoday"},{"key":"2026091409072970600_cit4","unstructured":"Kusner, M. J.; Paige, B.; Herna\u0301ndez-Lobato, J. M. In  Grammar\nVariational Autoencoder, International conference on machine\nlearning; PMLR, 2017; pp 1945\u20131954."},{"key":"2026091409072970600_cit5","unstructured":"Jin, W.; Barzilay, R.; Jaakkola, T. In  Junction Tree Variational\nAutoencoder for Molecular Graph Generation, International\nconference on machine learning; PMLR, 2018; pp 2323\u20132332."},{"key":"2026091409072970600_cit6","unstructured":"Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P. L. C.; Aspuru-Guzik, A.\n          Objective-reinforced\ngenerative adversarial networks (organ) for sequence generation models. 2017, arXiv:1705.10843. arXiv.org e-Print archive. http:\/\/arxiv.org\/abs\/1705.10843."},{"key":"2026091409072970600_cit7","unstructured":"De Cao, N.; Kipf, T.\n          Molgan: An implicit generative\nmodel for small molecular graphs. 2018, arXiv:1805.11973. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/1805.11973."},{"key":"2026091409072970600_cit8","doi-asserted-by":"crossref","unstructured":"Zang, C.; Wang, F. In  Moflow:\nAn Invertible Flow Model\nfor Generating Molecular Graphs, Proceedings of the 26th\nACM SIGKDD international conference on knowledge discovery & data\nmining; ACM Digital Library, 2020; pp 617\u2013626.","DOI":"10.1145\/3394486.3403104"},{"key":"2026091409072970600_cit9","doi-asserted-by":"publisher","first-page":"549","DOI":"10.52202\/075280-0026","article-title":"Equivariant flow\nmatching with hybrid probability transport for 3d molecule generation","volume":"36","author":"Song","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"2026091409072970600_cit10","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3ffb","article-title":"Chemformer: a pre-trained\ntransformer for computational chemistry","volume":"3","author":"Irwin","year":"2022","journal-title":"Mach.\nLearn.: Sci. Technol."},{"issue":"1","key":"2026091409072970600_cit11","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00812-5","article-title":"Reinvent 4: Modern\nai\u2013driven generative molecule\ndesign","volume":"16","author":"Loeffler","year":"2024","journal-title":"J. Cheminf."},{"issue":"18","key":"2026091409072970600_cit12","doi-asserted-by":"publisher","first-page":"7086","DOI":"10.1021\/acs.jcim.4c01396","article-title":"Large language\nmodels\nas molecular design engines","volume":"64","author":"Bhattacharya","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"2026091409072970600_cit13","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-025-00984-8","article-title":"Large language models\nopen new way of ai-assisted molecule design for chemists","volume":"17","author":"Ishida","year":"2025","journal-title":"J. Cheminf."},{"key":"2026091409072970600_cit14","unstructured":"Hoogeboom, E.; Satorras, V. G.; Vignac, C.; Welling, M. In  Equivariant Diffusion\nfor Molecule Generation in 3d, International conference on\nmachine learning; PMLR, 2022; pp 8867\u20138887."},{"key":"2026091409072970600_cit15","unstructured":"Xu, M.; Yu, L.; Song, Y.; Shi, C.; Ermon, S.; Tang, J.\n          Geodiff: A geometric diffusion model for molecular conformation generation. 2022,arXiv:2203.02923. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2203.02923."},{"issue":"12","key":"2026091409072970600_cit16","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1038\/s43588-024-00737-x","article-title":"Structure-based\ndrug design with equivariant diffusion\nmodels","volume":"4","author":"Schneuing","year":"2024","journal-title":"Nat. Comput. Sci."},{"key":"2026091409072970600_cit17","unstructured":"Guan, J.; Qian, W. W.; Peng, X.; Su, Y.; Peng, J.; Ma, J. In  3d Equivariant Diffusion for Target-Aware Molecule Generation\nand Affinity Prediction, International Conference on Learning\nRepresentations; ICLR, 2023."},{"issue":"1","key":"2026091409072970600_cit18","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad435","article-title":"Kgdiff: towards explainable\ntarget\u2013aware molecule generation with knowledge guidance","volume":"25","author":"Qian","year":"2023","journal-title":"Briefings Bioinf."},{"key":"2026091409072970600_cit19","unstructured":"Guan, J.; Zhou, X.; Yang, Y.; Bao, Y.; Peng, J.; Ma, J.; Liu, Q.; Wang, L.; Gu, Q.\n          Decompdiff: diffusion models\nwith decomposed priors for structure\u2013based drug design. 2024, arXiv:2403.07902. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2403.07902."},{"key":"2026091409072970600_cit20","unstructured":"Wu, J.; Qiao, A.; Wang, Z.; Wei, Z.; Chen, S.\n          Fusediff: Symmetry\u2013preserving joint diffusion\nfor dual-target structure-based drug design. 2026, arXiv:2603.05567. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2603.05567."},{"issue":"7743","key":"2026091409072970600_cit21","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","article-title":"Ultra-large library\ndocking for discovering\nnew chemotypes","volume":"566","author":"Lyu","year":"2019","journal-title":"Nature"},{"issue":"7805","key":"2026091409072970600_cit22","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1038\/s41586-020-2117-z","article-title":"An open-source drug\ndiscovery platform enables ultra-large virtual screens","volume":"580","author":"Gorgulla","year":"2020","journal-title":"Nature"},{"issue":"18","key":"2026091409072970600_cit23","doi-asserted-by":"publisher","first-page":"16796","DOI":"10.1021\/acs.jmedchem.4c01632","article-title":"Identifying\nartifacts from large library docking","volume":"67","author":"Wu","year":"2024","journal-title":"J. Med.\nChem."},{"key":"2026091409072970600_cit24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/S0959-8049(01)00231-3","article-title":"Egfr and cancer\nprognosis","volume":"37","author":"Nicholson","year":"2001","journal-title":"Eur. J. Cancer"},{"issue":"1","key":"2026091409072970600_cit25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1146\/annurev-pathol-011110-130206","article-title":"Egfr mutations and\nlung cancer","volume":"6","author":"da Cunha Santos","year":"2011","journal-title":"Annu. Rev. Pathol.: Mech. Dis."},{"key":"2026091409072970600_cit26","unstructured":"Ho, J.; Jain, A.; Abbeel, P. In  Denoising Diffusion Probabilistic\nModels, Advances in Neural Information Processing Systems\n33; NeurIPS, 2020; pp 6840\u20136851."},{"key":"2026091409072970600_cit27","unstructured":"Song, J.; Meng, C.; Ermon, S.\n          Denoising diffusion implicit models. 2020, arXiv:2010.02502. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2010.02502."},{"issue":"4","key":"2026091409072970600_cit28","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1007\/s11633-025-1562-4","article-title":"Dpm-solver++: Fast\nsolver for guided sampling of diffusion probabilistic\nmodels","volume":"22","author":"Lu","year":"2025","journal-title":"Mach. Intell. Res."},{"issue":"9","key":"2026091409072970600_cit29","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1002\/cmdc.200600155","article-title":"Prediction\nof admet properties","volume":"1","author":"Norinder","year":"2006","journal-title":"ChemMedChem"},{"issue":"1","key":"2026091409072970600_cit30","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8","article-title":"Estimation\nof synthetic accessibility score of drug-like\nmolecules based on molecular complexity and fragment contributions","volume":"1","author":"Ertl","year":"2009","journal-title":"J. Cheminf."},{"issue":"2","key":"2026091409072970600_cit31","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying\nthe chemical beauty of drugs","volume":"4","author":"Bickerton","year":"2012","journal-title":"Nature Chem."},{"issue":"2","key":"2026091409072970600_cit32","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1021\/acs.jctc.0c01006","article-title":"Accelerating\nAutoDock4 with GPUs and gradient-based local search","volume":"17","author":"Santos-Martins","year":"2021","journal-title":"J. Chem. Theory Comput."},{"issue":"9","key":"2026091409072970600_cit33","doi-asserted-by":"publisher","first-page":"3130","DOI":"10.1039\/D3SC04185A","article-title":"Posebusters:\nAi-based docking methods\nfail to generate physically valid poses or generalise to novel sequences","volume":"15","author":"Buttenschoen","year":"2024","journal-title":"Chem. Sci."},{"issue":"1","key":"2026091409072970600_cit34","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s42514-025-00256-9","article-title":"Revisiting\nworkflow execution in hpc: a data-flow approach","volume":"8","author":"Chen","year":"2026","journal-title":"CCF Trans. High Perform. Comput."},{"issue":"D1","key":"2026091409072970600_cit35","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"Chembl:\na large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026091409072970600_cit36","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The\nchembl Database\nin 2023: a drug discovery platform spanning multiple bioactivity data\ntypes and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res."},{"issue":"W1","key":"2026091409072970600_cit37","doi-asserted-by":"publisher","first-page":"W530","DOI":"10.1093\/nar\/gkab294","article-title":"Plip 2021: expanding the scope of\nthe protein-ligand interaction profiler to dna and rna","volume":"49","author":"Adasme","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"W1","key":"2026091409072970600_cit38","doi-asserted-by":"publisher","first-page":"W463","DOI":"10.1093\/nar\/gkaf361","article-title":"Plip 2025:\nintroducing protein-protein interactions to the protein-ligand interaction\nprofiler","volume":"53","author":"Schake","year":"2025","journal-title":"Nucleic Acids Res."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.6c01357","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/17\/11024\/67049460\/acs.jcim.6c01357.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/17\/11024\/67049460\/acs.jcim.6c01357.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:26:42Z","timestamp":1789392402000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/66\/17\/11024\/5292068\/DASH-A-Pocket-Aware-and-Objective-Aware-Framework"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,21]]},"references-count":38,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,8,21]]},"published-print":{"date-parts":[[2026,9,14]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.6c01357","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,21]]}}}