{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:04:09Z","timestamp":1773803049122,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"25","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure\u2013property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure\u2013property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG.<\/jats:p>","DOI":"10.1609\/aaai.v40i25.39245","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:22:55Z","timestamp":1773796975000},"page":"21029-21037","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing"],"prefix":"10.1609","volume":"40","author":[{"given":"Ziyu","family":"Fan","sequence":"first","affiliation":[]},{"given":"Zhijian","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yahan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaowen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Siyuan","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Yunliang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zeyu","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Shuhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shuning","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shangqian","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Min","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39245\/43206","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39245\/43206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:22:55Z","timestamp":1773796975000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i25.39245","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}