计算机辅助的药物发现用于无法药物治疗的目标
Qi Sun1,2, Hanping Wang1, Juan Xie3
1BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Chemical reviews
|May 27, 2025
概括
计算和人工智能策略正在彻底改变药物设计,以挑战"不可抗药"的目标. 这些先进的方法为以前认为无法治疗的疾病提供了新的治疗途径.
科学领域:
- 计算化学和药物发现
- 医学中的人工智能
- 生物化学和结构生物学.
背景情况:
- 由于其独特的结构和功能特征,难以治疗的目标带来了重大治疗挑战.
- 传统的药物设计方法与缺乏明确的口袋或涉及动态相互作用的目标作斗争.
- 蛋白质与蛋白质的相互作用和内在无序的蛋白质代表了关键的不可抗药的标类.
研究的目的:
- 审查最近的计算和人工智能驱动的进步,设计药物对抗难以药物治疗的目标.
- 突出成功的案例研究和这一领域的新兴策略.
- 讨论未来的方向和扩大可药物化蛋白质组的剩余挑战.
主要方法:
- 对计算模拟和人工智能方法的审查.
- 分析AI在结构预测,虚拟选和de novo连接体设计中的应用.
- 计算方法与实验验证技术的整合.
主要成果:
- 计算和人工智能方法正在实现针对以前无法药物治疗的蛋白质的新策略.
- 成功的案例研究证明了这些先进方法的有效性.
- 人工智能显著提高了蛋白质 - 连接体复合物的预测和对具有挑战性的目标的连接体生成.
结论:
- 先进的计算和人工智能工具对于克服药物设计的局限性至关重要.
- 整合计算和实验方法有望进一步突破.
- 这些进展通过扩大可用药物的空间,扩大了难以治疗的疾病的治疗机会.
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