{"id":59,"date":"2026-06-17T00:52:29","date_gmt":"2026-06-16T22:52:29","guid":{"rendered":"https:\/\/journalofartificialintelligence.com\/ar\/2026\/06\/17\/%d8%ac%d8%b2%d9%8a%d8%a1-%d8%ac%d8%af%d9%8a%d8%af-%d9%88%d8%a7%d8%b9%d8%af-%d8%b6%d8%af-%d8%b3%d8%b1%d8%b7%d8%a7%d9%86-%d8%a7%d9%84%d8%b1%d8%a6%d8%a9-%d9%8a%d8%b3%d8%aa%d9%87%d8%af%d9%81-egfr\/"},"modified":"2026-06-17T00:52:54","modified_gmt":"2026-06-16T22:52:54","slug":"%d8%ac%d8%b2%d9%8a%d8%a1-%d8%ac%d8%af%d9%8a%d8%af-%d9%88%d8%a7%d8%b9%d8%af-%d8%b6%d8%af-%d8%b3%d8%b1%d8%b7%d8%a7%d9%86-%d8%a7%d9%84%d8%b1%d8%a6%d8%a9-%d9%8a%d8%b3%d8%aa%d9%87%d8%af%d9%81-egfr","status":"publish","type":"post","link":"https:\/\/journalofartificialintelligence.com\/ar\/2026\/06\/17\/%d8%ac%d8%b2%d9%8a%d8%a1-%d8%ac%d8%af%d9%8a%d8%af-%d9%88%d8%a7%d8%b9%d8%af-%d8%b6%d8%af-%d8%b3%d8%b1%d8%b7%d8%a7%d9%86-%d8%a7%d9%84%d8%b1%d8%a6%d8%a9-%d9%8a%d8%b3%d8%aa%d9%87%d8%af%d9%81-egfr\/","title":{"rendered":"\u062c\u0632\u064a\u0621 \u062c\u062f\u064a\u062f \u0648\u0627\u0639\u062f \u0636\u062f \u0633\u0631\u0637\u0627\u0646 \u0627\u0644\u0631\u0626\u0629 \u064a\u0633\u062a\u0647\u062f\u0641 EGFR"},"content":{"rendered":"

\"\u062c\u0632\u064a\u0621“`html<\/p>\n

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“`<\/p>\n

\n

Sources et cr\u00e9dits<\/h2>\n

\u00c9tude source<\/h3>\n

DOI\u00a0:<\/strong> https:\/\/doi.org\/10.1007\/s11030-026-11625-z<\/a><\/p>\n

Titre\u00a0:<\/strong> Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer<\/p>\n

Revue : <\/strong> Molecular Diversity<\/p>\n

\u00c9diteur : <\/strong> Springer Science and Business Media LLC<\/p>\n

Auteurs : <\/strong> Hailing Qie; Liyuan Wang; Ce Li; Chen Wu; Yong Wang; Kuo Xiao; Lili Diao<\/p>\n","protected":false},"excerpt":{"rendered":"

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