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<p>The increase in multidrug-resistant bacteria underscores an urgent need for additional antibiotics. Here, we integrate small-molecule high-throughput screening with a deep-learning-based virtual screening approach to uncover new antibacterial compounds. We screen ~2 million small molecules against a sensitized <i>Escherichia coli</i> strain, yielding thousands of hits. We use these data to train a deep learning model, GNEprop, to predict antibacterial activity, retrospectively validating robustness with respect to out-of-distribution generalization and activity cliff prediction. Virtual screening of over 1.4 billion synthetically accessible compounds identifies potential candidates, of which 82 exhibit antibacterial activity on the same strain, illustrating a 90-fold improved hit rate over the high-throughput screening experiment used for training. Many newly identified compounds exhibit high dissimilarity to known antibiotics, potency beyond the training bacterial strain and selectivity. Biological characterization identifies specific, validated targets, indicating promising avenues for further exploration in antibiotic discovery.</p>

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Deep-learning-based virtual screening of antibacterial compounds

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Gabriele Scalia,
  • Steven T. Rutherford,
  • Ziqing Lu,
  • Kerry R. Buchholz,
  • Nicholas Skelton,
  • Kangway Chuang,
  • Nathaniel Diamant,
  • Jan-Christian Hütter,
  • Jerome-Maxim Luescher,
  • Anh Miu,
  • Jeff Blaney,
  • Leo Gendelev,
  • Elizabeth Skippington,
  • Greg Zynda,
  • Nia Dickson,
  • Michał Koziarski,
  • Yoshua Bengio,
  • Aviv Regev,
  • Man-Wah Tan,
  • Tommaso Biancalani

摘要

The increase in multidrug-resistant bacteria underscores an urgent need for additional antibiotics. Here, we integrate small-molecule high-throughput screening with a deep-learning-based virtual screening approach to uncover new antibacterial compounds. We screen ~2 million small molecules against a sensitized Escherichia coli strain, yielding thousands of hits. We use these data to train a deep learning model, GNEprop, to predict antibacterial activity, retrospectively validating robustness with respect to out-of-distribution generalization and activity cliff prediction. Virtual screening of over 1.4 billion synthetically accessible compounds identifies potential candidates, of which 82 exhibit antibacterial activity on the same strain, illustrating a 90-fold improved hit rate over the high-throughput screening experiment used for training. Many newly identified compounds exhibit high dissimilarity to known antibiotics, potency beyond the training bacterial strain and selectivity. Biological characterization identifies specific, validated targets, indicating promising avenues for further exploration in antibiotic discovery.