The rise of pan-resistant bacteria is creating an urgent need for structurally novel antibiotics. Artificial intelligence methods can discover new antibiotics, but existing methods have notable limitations. Property prediction models, which evaluate molecules one-by-one for a given property, scale poorly to large chemical spaces. Generative models, which directly design molecules, rapidly explore vast chemical spaces but generate molecules that are challenging to synthesize. Here we introduce SyntheMol, a generative model that designs new compounds, which are easy to synthesize, from a chemical space of nearly 30 billion molecules. We apply SyntheMol to design molecules that inhibit the growth of Acinetobacter baumannii, a burdensome Gram-negative bacterial pathogen. We synthesize 58 generated molecules and experimentally validate them, with six structurally novel molecules demonstrating antibacterial activity against A. baumannii and several other phylogenetically diverse bacterial pathogens. This demonstrates the potential of generative artificial intelligence to design structurally novel, synthesizable and effective small-molecule antibiotic candidates from vast chemical spaces, with empirical validation.

泛耐药细菌的兴起迫切需要结构新颖的抗生素。人工智能方法可以发现新的抗生素，但现有方法有明显的局限性。性质预测模型逐一评估分子的给定性质，很难扩展到大型化学空间。直接设计分子的生成模型可以快速探索广阔的化学空间，但生成的分子难以合成。在这里，我们介绍 SyntheMol，这是一种生成模型，可以从近 300 亿个分子的化学空间中设计易于合成的新化合物。我们应用 SyntheMol 来设计抑制鲍曼不动杆菌（一种负担重的革兰氏阴性细菌病原体）生长的分子。我们合成了 58 个生成的分子并通过实验验证了它们，其中 6 个结构新颖的分子表现出对鲍曼不动杆菌和其他几种系统发育多样化的细菌病原体的抗菌活性。这表明生成人工智能具有从广阔的化学空间设计结构新颖、可合成且有效的小分子候选抗生素的潜力，并经过实证验证。