{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:00:50Z","timestamp":1790164850621,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Artificial intelligence (AI) is reshaping molecular oncology by making complex biological data tractable and by extending the reach of precision drug discovery. Its emerging value is particularly evident at the interface of immunoengineering and targeted therapy, where computational models can help prioritize tumor-selective antigens, predict neoantigen immunogenicity, optimize engineered receptors, support antibody and antibody\u2013drug conjugate design, guide small-molecule discovery, refine genome-editing strategies, and anticipate treatment response. Across these applications, the central promise is not automation for its own sake, but a wider therapeutic window in which tumor control is increased while off-target toxicity is reduced. Yet the translational gap remains substantial. Many models are still preclinical, external validation is limited, and clinical implementation remains uncommon. This Commentary follows representative methods across a therapeutic continuum that now extends from target discovery and therapeutic engineering to imaging, liquid biopsy, resistance forecasting, combination selection, and drug repurposing. We argue that clinical value will depend on closed-loop workflows in which multimodal predictions are experimentally validated, externally tested, and longitudinally updated to guide the next therapeutic decision.<\/jats:p>","DOI":"10.3390\/ijms27198489","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:09:08Z","timestamp":1790161748000},"page":"8489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence as a Catalyst for Novel Therapeutic Opportunities in Precision Oncology"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9620-745X","authenticated-orcid":false,"given":"Kostas A.","family":"Papavassiliou","sequence":"first","affiliation":[{"name":"First University Department of Respiratory Medicine, \u2018Sotiria\u2019 Chest Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9882-9478","authenticated-orcid":false,"given":"Amalia A.","family":"Sofianidi","sequence":"additional","affiliation":[{"name":"Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angeliki","family":"Margoni","sequence":"additional","affiliation":[{"name":"Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5803-4527","authenticated-orcid":false,"given":"Athanasios G.","family":"Papavassiliou","sequence":"additional","affiliation":[{"name":"Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1200\/EDBK_174176","article-title":"Precision Oncology: Who, How, What, When, and When Not?","volume":"37","author":"Schwartzberg","year":"2017","journal-title":"Am. 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