高通量计算选用于领先的发现和开发.
Neelufar Shama Shaik1, Harika Balya2
1Department of Pharmacognosy, Scient Institute of Pharmacy, Ibrahimpatnam, R.R. District, Telangana, India.
Advances in pharmacology (San Diego, Calif.)
|April 2, 2025
概括
高通量计算选 (HTCS) 通过虚拟选数百万种化合物来加速药物发现. 这种人工智能驱动的方法增强了领先的识别和个性化药物开发.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 生物信息学和系统生物学
背景情况:
- 传统的药物发现是耗时和昂贵的.
- 高通量选 (HTS) 产生了大量的数据集,但可能是资源密集的.
- 计算方法为化合物识别提供了更快,更具成本效益的替代方案.
研究的目的:
- 突出高通量计算查 (HTCS) 在药物发现中的变革性影响.
- 讨论HTCS的核心方法和新兴的人工智能驱动的进展.
- 解决HTCS在开发个性化医疗方面的挑战和未来潜力.
主要方法:
- 使用先进的算法对庞大的化学图书馆进行虚拟选.
- 将omics数据 (基因组学,蛋白质组学,代谢组学) 集成到计算管道中.
- 分子对接的应用,定量结构-活性关系 (QSAR) 模型和药模拟.
- 利用机器学习 (ML) 和人工智能 (AI) 来提高预测和模式识别.
- 使用计算工具来生成新化学实体的新药设计.
主要成果:
- 通过减少时间,成本和劳动力,HTCS显著加速早期药物发现.
- 人工智能和机器学习的整合提高了预测准确度,并揭示了复杂的分子模式.
- 计算方法越来越多地用于新药设计,创造优化的化合物.
- HTCS有助于更深入地了解生物系统,从而实现个性化医疗方法.
结论:
- 通过快速识别和优化化合物,HTCS正在彻底改变药物发现.
- 尽管在数据质量和验证方面存在挑战,HTCS有望成为现代药物发现的基石.
- 未来人工智能,量子计算和大数据分析方面的进步将进一步增强HTCS的精确和高效的治疗策略.
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