准确和高效的循环药物样分子与反向动力学模拟样本采集
Nikolai V Krivoshchapov1, Michael G Medvedev1
1N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russian Federation.
Journal of chemical information and modeling
|May 22, 2024
概括
一个名为MCR的新算法有效地采样分子构造,这对于药物发现至关重要. 它在复杂的循环分子的速度和多样性方面优于现有的方法.
科学领域:
- 计算生化学计算生化学
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 识别生物分子适配体对于计算生物化学任务,如分子对接,至关重要.
- 分子中的灵活循环阻碍了使用传统方法高效的构造性搜索.
- 精确的形状组合对于优化中的候选药物至关重要.
研究的目的:
- 开发一种新的算法,以高效地进行 (多) 循环分子的全球形状采样.
- 克服现有方法在处理分子灵活性方面的局限性.
- 提高药物开发适合器生成的速度和多样性.
主要方法:
- 开发了一种基于反动力学的蒙特卡洛精细化 (MCR) 算法.
- 在循环系统中,MCR识别了可独立旋转的二面角.
- 用于复杂分子的全球形状采样的MCR.
主要成果:
- 在速度和合规组合多样性方面,MCR显著优于MacroModel,CREST和RDKit.
- 该算法有效地处理灵活的循环,这是结构分析中常见的挑战.
- 对于大多数测试的分子,MCR准确地恢复了自然的宏循环构造.
结论:
- MCR算法为 (多) 循环生物分子的构型采样提供了一种优越的方法.
- 这种方法提高了计算药物发现管道的效率和准确性.
- 在产生多样化和相关的分子构造方面,MCR代表了重大进步.
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