在激酶抑制剂开发中利用人工智能和机器学习:进展,挑战和未来前景
Mohamed S Elgawish1,2, Aya M Almatary3, Sawsan A Zaitone4,5
1Medicinal Chemistry Department, Faculty of Pharmacy, Suez Canal University Ismailia 41522 Egypt mohamed_elgawish@pharm.suez.edu.eg +20643230741 +82109893 8184.
RSC medicinal chemistry
|September 8, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在通过解决目标识别和抵抗等挑战,彻底改变激酶抑制剂药物发现. 这些计算方法加速了下一代酶向治疗的发展.
科学领域:
- 生物化学和药理学 生物化学和药理学
- 计算生物学和生物信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 蛋白激酶是关键的细胞信号调节器,与癌症和自身免疫性疾病有关.
- 像伊马替尼布这样的激酶抑制剂证明了治疗价值,但在开发选择性和强效药物方面仍然存在挑战.
- 保存的ATP结合部位,非目标效应,耐药性和患者的可变性使酶抑制剂的发展复杂化.
研究的目的:
- 审查人工智能和机器学习在设计,优化和改造激酶抑制剂中的变革性应用.
- 探索AI/ML方法如何解决酶向药物发现的关键障碍.
- 突出AI/ML在加速新激酶抑制剂开发方面的潜力.
主要方法:
- 审查AI/ML方法,包括深度学习,图形神经网络和生成模型.
- 在药物发现管道中分析AI/ML应用:目标识别,虚拟查,SAR建模,耐药性预测和临床试验设计.
- 检查展示AI优化的激酶抑制剂 (例如BTK,EGFR) 的案例研究.
主要成果:
- 人工智能/ML方法为诸如目标识别,虚拟查和预测耐药性等挑战提供了解决方案.
- 案例研究表明,人工智能在优化激酶抑制剂方面具有现实影响.
- 目前的局限性包括数据稀疏性,模型可解释性以及计算和实验结果之间的差距.
结论:
- 与药物化学的AI/ML整合有望加速和完善激酶抑制剂的开发.
- 新兴方向包括联合学习,个性化激酶抑制剂和支持人工智能的组合疗法.
- 人工智能/ML具有很大的潜力,可以推动下一代酶向治疗的发展.
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