This AI method could turbocharge the hunt for new medicines

Nature NewsCenterEN 3 min read 100% complete by Heidi LedfordOctober 25, 2025 at 04:27 PM

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Researchers have developed an artificial intelligence (AI) model called DrugReflector that could accelerate drug discovery by analyzing how chemical compounds affect gene activity in human cells. The deep-learning method, detailed in a study published on October 23 in Science, was trained using data from nearly 9,600 chemical compounds and over 50 cell types. Traditionally, drug discovery involves laborious screening of large chemical libraries to identify potential therapies. This new AI approach could streamline the process by predicting compound effects without extensive physical testing, potentially opening new avenues for developing medicines more efficiently.

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Massachusetts Institute of Technology 40% Peking University 50% Alex Shalek 60% Heidi Ledford 70% Hongkui Deng 80%

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artificial intelligence 90% drug discovery 85% gene expression data 75% deep learning model 70% chemical compounds 65% screening system 60% drug discovery process 55% gene activity networks 50% biomedical engineering 45% platelets generation 40%

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artificial intelligence drug discovery gene expression data deep learning model chemical compounds screening system drug discovery process gene activity networks biomedical engineering platelets generation

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