✉news ScienceBiology first seen 1 d ago, last 21 h ago, peak #30

AI Method Chooses Its Own Training Data to Improve Drug–Target Prediction

Original: AI Learns to Pick Its Own Lessons: Complexity-Aware Active Learning Boosts Drug–Target Prediction

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 now: New machine learning research promises faster, cheaper early-stage drug discovery, a topic of broad interest in biotech and pharma.

drug–target interaction predictionactive learningartificial intelligencedrug discovery

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Evidence

• AI Learns to Pick Its Own Lessons: Complexity-Aware Active Learning Boosts Drug–Target Prediction · Bioengineer.org

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