癌症研究中的计算创新以及计算机如何改变药物发现和开发:一篇评论
Ilavanalangki Lytan1, Basanta Singha1, Nichan Boruah1
1Department of Chemistry, Nagaland University, Lumami, Zunheboto, Nagaland, 798627, India.
Mini reviews in medicinal chemistry
|March 15, 2026
概括
计算机辅助药物设计 (CADD) 通过合理设计和预测建模来加速癌症药物发现. 将CADD与AI,ML和多omics数据集成,有望提供更有效,个性化的癌症疗法.
科学领域:
- 在瘤学瘤学.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 由于癌症的复杂性和耐治疗性,癌症仍然是全球主要的死亡原因.
- 癌症研究的传统实验方法通常是缓慢的,昂贵的,缺乏预测准确度.
研究的目的:
- 审查计算机辅助药物设计 (CADD) 和相关计算方法在推进癌症研究和治疗方面的影响.
- 突出CADD如何加速抗癌药物的药物发现,标识和化合物优化.
主要方法:
- 在癌症研究中对CADD研究的文献综述.
- 专注于CADD在药物发现,目标识别和化合物优化中的应用.
- 在癌症治疗开发中检查人工智能,机器学习和量子计算等计算创新.
主要成果:
- CADD和计算方法显著提高了癌症药物发现,加速了目标识别和引优化.
- 人工智能/ML模型,分子对接,动态,QSAR和虚拟查改善了对疗效,毒性和耐药性的预测.
- 多omics数据的整合有助于生物标志物发现和患者分层,案例研究表明,发展时间缩短,对EGFR,PARP和KRAS G12C等目标有更好的选择性.
结论:
- 通过加快药物发现和设计更有选择性的抗癌剂,CADD正在彻底改变癌症研究.
- 计算方法,包括AI,ML和量子计算,使合理的药物设计和预测建模成为可能.
- 整合基因组学,蛋白质组学,成像和计算方法对于开发更有效,个性化的癌症疗法至关重要.
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