Discovery of a structural class of antibiotics with explainable deep learning

By Felix Wong, Erica J. Zheng, Jacqueline A. Valeri, Nina M. Donghia, Melis N. Anahtar, Satotaka Omori, Alicia Li, Andres Cubillos-Ruiz, Aarti Krishnan, Wengong Jin and 11 more. Nature, 2023

394 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology

View Paper: https://www.semanticscholar.org/paper/a2035b334dc6e1b676eb24d729f68c2c31ab051d

Citations in the corpus

• Artificial intelligence in drug development (2025, neutral): “A recent study used nuclear morphology and machine learning to identify compounds inducing senescence in cancer cells 73 ; similar strategies are also promising for antibiotic discovery 68 .”
• Empowering Biomedical Discovery with AI Agents (2024, neutral): “For a level 2, the long-term objective is its function as a collaborator for scientists through excelling at tasks that are explicit continuations of existing scientific research, such as improving the efficiency of chemical probes, autonomously designing and testing de novo enzymes, or designing new…”