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Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with non-small cell lung cancer being the most common form. Among the key factors in its development, mutations in the epidermal growth factor receptor, or EGFR, play a central role. This receptor, involved in regulating cell growth and survival, can, when mutated, cause uncontrolled cell proliferation, thereby promoting tumor progression.<\/p>\n
Current EGFR-targeted treatments, such as gefitinib, have improved clinical outcomes for patients with these mutations. However, the emergence of resistance and side effects limits their long-term effectiveness. In light of this, the search for new molecules capable of specifically blocking the activity of mutated EGFR has become a priority.<\/p>\n
A recent breakthrough has led to the discovery of a new family of compounds, quinoline-sulfonamides, through an innovative approach combining artificial intelligence and experimental validation. This method uses generative models based on reinforcement learning to explore the chemical space and identify promising molecules. Among these, a molecule named Hit1 has stood out for its effectiveness.<\/p>\n
Hit1 has demonstrated a strong ability to inhibit EGFR kinase activity, with a median inhibitory concentration of 21.2 nanomoles per liter. Although less potent than gefitinib, this molecule has shown marked antiproliferative activity against non-small cell lung cancer cells harboring EGFR mutations, such as the PC9 and HCC827 cell lines. In contrast, its effect on wild-type cells, such as A549, is much lower, suggesting increased selectivity for mutated cells.<\/p>\n
Molecular dynamics simulations have revealed that Hit1 binds efficiently to EGFR by forming hydrogen bonds with a key residue, lysine 745, and by occupying a hydrophobic cavity located near valine 726. These interactions stabilize the molecule in the ATP binding pocket, thereby preventing receptor activation. Additionally, Hit1 exhibited a balanced pharmacokinetic profile, with good solubility and favorable membrane permeability, making it a serious candidate for further development.<\/p>\n
This discovery highlights the potential of hybrid approaches, combining artificial intelligence and experimental validation, to accelerate the design of new drugs. Hit1, with its accessible scaffold and targeted properties, paves the way for a new generation of EGFR inhibitors that are more selective and better tolerated.<\/p>\n
DOI:<\/strong> https:\/\/doi.org\/10.1007\/s11030-026-11625-z<\/a><\/p>\n Title:<\/strong> Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer<\/p>\n Journal:<\/strong> Molecular Diversity<\/p>\n Publisher:<\/strong> Springer Science and Business Media LLC<\/p>\n Authors:<\/strong> Hailing Qie; Liyuan Wang; Ce Li; Chen Wu; Yong Wang; Kuo Xiao; Lili Diao<\/p>\n","protected":false},"excerpt":{"rendered":" A promising new molecule against EGFR-targeted lung cancer Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with non-small cell lung cancer being the most common form. Among the key factors in its development, mutations in the epidermal growth factor receptor, or EGFR, play a central role. This receptor, involved in regulating… Continue reading A promising new molecule against EGFR-targeted lung cancer<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,7],"tags":[],"class_list":["post-63","post","type-post","status-publish","format-standard","hentry","category-health","category-international","entry"],"_links":{"self":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/63","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/comments?post=63"}],"version-history":[{"count":1,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/63\/revisions"}],"predecessor-version":[{"id":64,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/63\/revisions\/64"}],"wp:attachment":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/media?parent=63"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/categories?post=63"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/tags?post=63"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}