药物发现和设计中的创新计算方法
Itishree Jogamaya Das1, Kalpita Bhatta2, Itisam Sarangi3
1Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, Ranchi, India.
Advances in pharmacology (San Diego, Calif.)
|April 2, 2025
概括
先进的计算方法,包括人工智能和机器学习,正在改变药物发现. 这些工具可以加速开发,提高准确性,降低创造新药的成本.
科学领域:
- 计算化学和生物学计算化学和生物学
- 药理学和药物开发领域
- 生物信息学和化学信息学
背景情况:
- 随着COVID-19的爆发,人们越来越需要更快的药物发现.
- 传统药物开发是耗时的,昂贵的,并且失败率很高.
- 计算方法为这些挑战提供了一个有希望的解决方案.
研究的目的:
- 审查先进的计算方法对药物发现和设计的影响.
- 要突出AI,机器学习和其他计算工具如何加速和改进过程.
- 讨论各种计算方法的整合,以提高药物开发效率.
主要方法:
- 机器学习 (ML) 和人工智能 (AI) 用于预测建模和虚拟选.
- 分子动力学 (MD) 模拟用于理解蛋白质 - 连接体相互作用.
- 量子力学/分子力学 (QM/MM) 用于结合能量的预测.
- 基于结构的药物设计 (SBDD),网络药理学和系统生物学.
- 化学信息学,计算毒理学和生物信息学.
- 云计算和大数据技术用于高吞吐量分析.
主要成果:
- 机器学习和人工智能显著提高预测建模,虚拟选和de novo设计.
- 医学药物模拟提供了对药物疗效和相互作用的原子级洞察力.
- QM/MM方法可以准确地预测结合能量和反应机制.
- SBDD,网络药理学和化学信息学优化化合物特性并识别目标.
- 计算毒理学和生物信息学减少动物试验,并个性化药物反应.
- 大数据和云计算使高效的高通量选和分析成为可能.
结论:
- 计算方法正在彻底改变药物发现,使其更快,更准确.
- 多样化的计算工具的整合推动了制药研究的范式转变.
- 这些创新导致了更高效,更具成本效益和更精确的药物开发过程.
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