{"id":177479,"date":"2026-01-13T09:41:00","date_gmt":"2026-01-13T14:41:00","guid":{"rendered":"https:\/\/testing.news-you-need.com\/index.php\/2026\/01\/13\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules-news\/"},"modified":"2026-01-13T09:45:11","modified_gmt":"2026-01-13T14:45:11","slug":"ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules-news","status":"publish","type":"post","link":"https:\/\/testing.news-you-need.com\/index.php\/2026\/01\/13\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules-news\/","title":{"rendered":"AI tool dramatically reduces computing power needed to find protein-binding molecules | News"},"content":{"rendered":"<p><a href=\"https:\/\/www.chemistryworld.com\/news\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules\/4022757.article\">AI tool dramatically reduces computing power needed to find protein-binding molecules | News<\/a><\/p>\n<p><a href=\"https:\/\/www.chemistryworld.com\/news\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules\/4022757.article\">https:\/\/www.chemistryworld.com\/news\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules\/4022757.article<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-01-13 09:41:00<\/a><\/p>\n<p>Source Domain: <a href=\"www.chemistryworld.com\">www.chemistryworld.com<\/a><\/p>\n<ul>\n<li>A new machine learning tool called DrugCLIP dramatically speeds up the identification of small-molecule candidates that can bind specific proteins, requiring significantly less computational power compared to previous methods.<\/li>\n<li>Researchers at Tsinghua University developed the DrugCLIP framework, which uses a deep-learning algorithm to predict binding affinities, making it up to 10 million times faster than traditional docking methods.<\/li>\n<li>The method involves representing protein pockets and small molecules as vectors in high-dimensional space, calculating the scalar product to predict binding affinity, and validating the top candidates with AlphaFold3.<\/li>\n<li>Using the new tool, researchers identified potential drug candidates for the serotonin 2A receptor and the norepinephrine transporter, with one molecule targeting the norepinephrine transporter showing promising chemical effectiveness.<\/li>\n<li>The team discovered a new molecule that they hope to take to clinical trials, although they caution about the uncertainty of clinical effectiveness.<\/li>\n<li>Nicholas Polizzi at Harvard Medical School sees the potential of high-throughput, genome-wide screening for identifying ligands with minimal off-target effects, though he raises concerns regarding the generalizability of the deep-learning algorithm.<\/li>\n<\/ul>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI tool dramatically reduces computing power needed to find protein-binding molecules | News https:\/\/www.chemistryworld.com\/news\/ai-tool-dramatically-reduces-computing-power-needed-to-find-protein-binding-molecules\/4022757.article 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