基于片段的药物设计的期望和未来的挑战
1UCL School of Pharmacy, Department of Pharmaceutical and Biological Chemistry, University College London, London, UK.
Expert opinion on drug discovery
|February 3, 2026
概括
基于碎片的药物发现 (FBDD) 使用人工智能和机器学习来加速治疗化合物的识别. 这些计算方法提高了化合物设计,预测相互作用,并扩大了药物开发的化学多样性.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学的计算化学
- 人工智能在医学中的应用
背景情况:
- 基于碎片的药物发现 (FBDD) 是识别治疗化合物的关键策略.
- 增长-合并-链接 (GML) 模型在FBDD中结合了实验和计算技术.
- 生成模型越来越多地用于设计新型化合物并预测它们的相互作用.
研究的目的:
- 突出FBDD的最新进展,重点关注in silico方法.
- 展示人工智能和机器学习 (AI-ML) 在加速药物发现中的作用.
- 讨论AI-ML如何增强化合物设计,性能优化和化学多样性.
主要方法:
- 应用AI-ML,包括深度学习,生成模型和强化学习.
- 使用VAE (变量自编码器) 和强化学习进行模拟创建和结构-活动关系 (SAR) 分析.
- 采用口袋意识的设计和大规模的虚拟查来针对药物开发.
主要成果:
- 人工智能ML显著加快了命中到的过程,并改善了药物的特性.
- 生成化学使新的设计成为可能,促进化学多样性和知识产权.
- 人工智能有助于碎片生成,口袋特定设计,并针对具有挑战性的蛋白质和PROTACs等模式.
结论:
- 人工智能正在通过自动化复合设计和提高预测准确度来改变FBDD.
- 人工智能-ML工具减少了开发时间和成本,同时扩大了FBDD的范围.
- 虽然实验验证仍然至关重要,但人工智能显著提高了FBDD的效率和有效性.
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