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Evans Leaders Fund","award":["42115"],"award-info":[{"award-number":["42115"]}]},{"name":"NSERC Undergraduate Student Research Award"},{"name":"CEWIL Canada Innovation Hub program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug discovery is a time-consuming, expensive, and high-risk process. Recent advances in artificial intelligence (AI) have enabled major breakthroughs in small-molecule and protein therapeutics. However, AI-driven design of aptamer drugs remains largely unexplored. Aptamers are short (15\u2013100 nt) single-stranded DNAs or RNAs that exhibit high binding affinity, high specificity, and low immunogenicity, making them promising candidates for disease (such as cancer) therapeutics. Compared with protein\u2013ligand or protein\u2013protein systems, protein\u2013aptamer complexes are under-represented in public structural databases, and aptamers themselves are highly flexible and relatively large molecules. These characteristics present distinct challenges for AI-based structural modeling. Here, we systematically evaluate recent AI frameworks, including AlphaFold3, Chai-1, Boltz-2, and RoseTTAFold2NA, along with a template-based approach, in predicting protein\u2013aptamer complex structures and estimating binding free energies. We establish an independent benchmark to assess their performance in structural accuracy, stability, and energetic consistency. This study provides a foundation for the application of AI in aptamer drug design and offers a reference framework for future research in nucleic-acid therapeutics and biomolecular modeling.<\/jats:p>","DOI":"10.1093\/bib\/bbag206","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T11:30:04Z","timestamp":1776166204000},"source":"Crossref","is-referenced-by-count":4,"title":["Comprehensive evaluation of artificial intelligence-empowered approaches for protein\u2013aptamer complex prediction"],"prefix":"10.1093","volume":"27","author":[{"given":"Jiani","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Computer Science, Brock University , 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1 Ontario ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kha","family":"Tram","sequence":"additional","affiliation":[{"name":"Cytodiagnostics Inc. , 919 Fraser Dr Unit 11, Burlington, L7L 4X8 Ontario ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Brock University , 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1 Ontario ,","place":["Canada"]},{"name":"Department of Biological Sciences, Brock University , 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1 Ontario ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4873-6928","authenticated-orcid":false,"given":"Yifeng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brock University , 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1 Ontario ,","place":["Canada"]},{"name":"Department of Biological Sciences, Brock University , 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1 Ontario 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