人工智能和多omics在癌症药物发现的突破:一个审查
Israr Fatima1, Abdur Rehman1, Yanheng Ding1
1Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
European journal of medicinal chemistry
|October 8, 2024
概括
计算工具通过克服传统方法的局限性,加速癌症药物发现. 这些创新方法,包括机器学习,为开发向癌症治疗提供了新的途径.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 由于癌症的复杂性,遗传变异性和适应性,癌症对全球健康构成了重大挑战.
- 传统的癌症药物发现方法在开发新疗法的效率和成本效益方面存在局限性.
- 需要创新和适应性的方法对于对抗不断变化的癌症特征至关重要.
研究的目的:
- 审查计算机工具在现代癌症药物发现中的整合和影响.
- 检查目前用于瘤药物开发的计算技术的现状.
- 确定针对癌症治疗的目标癌症治疗方法的计算方法的挑战和未来方向.
主要方法:
- 计算技术的审查,包括分子对接,定量结构-活动关系 (QSAR) 模型,虚拟选和药模拟.
- 对机器学习和分子模拟用于药物发现的最新进展的分析.
- 在癌症研究背景下讨论这些计算策略的应用.
主要成果:
- 计算工具为克服传统药物发现过程中固有的障碍提供了强大的解决方案.
- 诸如分子对接和虚拟选等技术可以更有效地识别潜在的候选药物.
- 机器学习和分子模拟正在提高药物开发的预测准确性和速度.
结论:
- 计算方法对于开发有针对性且具有成本效益的癌症疗法具有变革性.
- 计算方法的持续创新对于解决癌症的复杂性至关重要.
- 整合计算工具是创造下一代癌症治疗方法的关键一步.
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