{"ok":true,"trend":{"id":879776,"platform":"news","region":"global","key":"ai learns to pick its own lessons: complexity-aware active learning boosts drug–target prediction","title":"AI Learns to Pick Its Own Lessons: Complexity-Aware Active Learning Boosts Drug–Target Prediction","url":"https://news.google.com/rss/articles/CBMivgFBVV95cUxPTDNFaEQzRU52NXBWUlJJeUUtMUktRDRlemN4bF9DTzlScklvTW9WVjFqNGNVS1lDQ1h6UDJqQ0FuVldXZGlZMnRnWFJMLVNkdEZBYUk0Mnlwd0VuT1g3M0JzblVSRWlBeWdjc0Y2VVU1TzBjWUpFdEJsLXZpRHlxdzcweE1XWlkwblk4MG80QU4yeVpLa0JuS1V0SGFSaHBSVnBjZWhDT1RFTFJkUjNWUF9ucHRGZ3oyVVJhRmZ3?oc=5","first_seen":"2026-10-03T16:28:22.668567Z","last_seen":"2026-10-04T00:42:56.632260Z","last_rank":36,"peak_rank":30,"last_volume":null,"peak_volume":0,"seen_count":6,"score":0.8015625,"category_hint":"biology","section":"science","category":"biology","summary":"Researchers report a complexity-aware active learning approach in which an AI system selects its own training examples, improving accuracy in drug–target interaction prediction. The method is described as a way to cut labelling costs and speed up early drug discovery by focusing computational effort on the most informative compounds and protein targets.","why":"New machine learning research promises faster, cheaper early-stage drug discovery, a topic of broad interest in biotech and pharma.","tone":"positive","entities":["drug–target interaction prediction","active learning","artificial intelligence","drug discovery"],"summarized_at":"2026-10-03T22:31:20.785150Z","meta":{"via":"scan","lang":"en","term":"biology","source":"bioengineer.org","published":"Sat, 03 Oct 2026 16:25:17 GMT"},"nw":null,"promo":null,"kind":null,"importance":null,"hidden":false,"hide_reason":null,"judged_at":null,"title_en":"AI Method Chooses Its Own Training Data to Improve Drug–Target Prediction","section_name":"Science","category_name":"Biology","timeline":[{"captured_at":"2026-10-03T16:28:22.668567Z","rank":35,"volume":null},{"captured_at":"2026-10-03T17:50:23.968898Z","rank":35,"volume":null},{"captured_at":"2026-10-03T19:12:24.764997Z","rank":34,"volume":null},{"captured_at":"2026-10-03T20:35:28.556031Z","rank":30,"volume":null},{"captured_at":"2026-10-03T21:58:17.506132Z","rank":33,"volume":null},{"captured_at":"2026-10-04T00:42:56.632260Z","rank":36,"volume":null}],"posts":[{"platform":"news","url":"https://news.google.com/rss/articles/CBMivgFBVV95cUxPTDNFaEQzRU52NXBWUlJJeUUtMUktRDRlemN4bF9DTzlScklvTW9WVjFqNGNVS1lDQ1h6UDJqQ0FuVldXZGlZMnRnWFJMLVNkdEZBYUk0Mnlwd0VuT1g3M0JzblVSRWlBeWdjc0Y2VVU1TzBjWUpFdEJsLXZpRHlxdzcweE1XWlkwblk4MG80QU4yeVpLa0JuS1V0SGFSaHBSVnBjZWhDT1RFTFJkUjNWUF9ucHRGZ3oyVVJhRmZ3?oc=5","author":"Bioengineer.org","title":"AI Learns to Pick Its Own Lessons: Complexity-Aware Active Learning Boosts Drug–Target Prediction","snippet":null,"posted_at":"2026-10-03T16:25:17Z","likes":null}],"elsewhere":[],"window":"7d"}}