Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

翻译：药物发现正逐步适应数据科学、信息学及人工智能等新兴技术，以加速有效疗法的开发，同时降低成本并减少动物实验。人工智能正在重塑药物发现领域，这一点从投资者、工业界与学术界科学家及立法者日益增长的兴趣中可见一斑。成功的药物发现需要优化药效学、药代动力学及临床结局相关属性。本综述聚焦小分子药物，探讨人工智能在药物发现三大支柱——疾病、靶点及治疗模式——中的应用。通过生成化学、机器学习及多属性优化等人工智能技术，已有多种化合物进入临床试验。科学界需严格核查已知信息以应对可重复性危机。人工智能在药物发现中的全部潜力，唯有在具备充分真实数据及后续研发阶段适当人为干预的条件下才能实现。