{"id":232214,"date":"2026-06-15T23:32:00","date_gmt":"2026-06-16T03:32:00","guid":{"rendered":"https:\/\/testing.news-you-need.com\/index.php\/2026\/06\/15\/penn-researchers-develop-predictive-ai-model-for-antibiotic-discovery\/"},"modified":"2026-06-16T00:00:15","modified_gmt":"2026-06-16T04:00:15","slug":"penn-researchers-develop-predictive-ai-model-for-antibiotic-discovery","status":"publish","type":"post","link":"https:\/\/testing.news-you-need.com\/index.php\/2026\/06\/15\/penn-researchers-develop-predictive-ai-model-for-antibiotic-discovery\/","title":{"rendered":"Penn researchers develop predictive AI model for antibiotic discovery"},"content":{"rendered":"<p><a href=\"https:\/\/www.thedp.com\/article\/2026\/06\/penn-engineering-antibiotic-development-artificial-intelligence-model-medicine-resistance\">Penn researchers develop predictive AI model for antibiotic discovery<\/a><\/p>\n<p><a href=\"https:\/\/www.thedp.com\/article\/2026\/06\/penn-engineering-antibiotic-development-artificial-intelligence-model-medicine-resistance\">https:\/\/www.thedp.com\/article\/2026\/06\/penn-engineering-antibiotic-development-artificial-intelligence-model-medicine-resistance<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-06-15 23:32:00<\/a><\/p>\n<p>Source Domain: <a href=\"www.thedp.com\">www.thedp.com<\/a><\/p>\n<ul>\n<li>Researchers at the School of Engineering and Applied Science have introduced an updated AI-powered model, ApexGO, to optimize the development of antibiotic candidates.<\/li>\n<li>ApexGO improves upon a previous model and can suggest modifications to existing molecules to enhance their effectiveness as antibiotics.<\/li>\n<li>Presidential Associate Professor C\u00e9sar de la Fuente highlighted ApexGO\u2019s ability to generate highly effective antimicrobial peptides, with 85% proving successful in real-world experiments and 72% demonstrating better performance against bacteria compared to their templates.<\/li>\n<li>Research assistant professor Marcelo Torres noted that ApexGO learns from biological sequences to prioritize candidates likely to possess antibacterial activity, focusing on promising but uncertain areas to improve search efficiency.<\/li>\n<li>Graduate student Natalie Maus emphasized that ApexGO helps narrow down the vast potential peptides for easier testing and demonstrated stronger antibacterial activity in mice than some FDA-approved last resort antibiotics.<\/li>\n<li>C\u00e9sar de la Fuente stressed the urgency of developing new antibiotics due to the rising threat of antimicrobial-resistant infections, which cause millions of deaths globally and may escalate in the coming decades.<\/li>\n<li>De la Fuente, Maus, and Torres all underscored the potential of ApexGO to make antibiotics discovery more systematic and to apply similar methodologies to broader peptide and protein engineering challenges.<\/li>\n<li>Natalie Maus highlighted the importance of open collaboration within the scientific community to tackle the growing problem of antibiotic resistance.<\/li>\n<\/ul>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Penn researchers develop predictive AI model for antibiotic discovery https:\/\/www.thedp.com\/article\/2026\/06\/penn-engineering-antibiotic-development-artificial-intelligence-model-medicine-resistance Publish Date: 2026-06-15 23:32:00 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