在罗塞塔 (REvoLd) 中使用进化算法对超大图书馆进行选
Paul Eisenhuth1,2, Fabian Liessmann3, Rocco Moretti4
1Institute for Drug Discovery, Leipzig University, Leipzig, Germany. eisenhuth@cs.uni-leipzig.de.
Communications chemistry
|November 7, 2025
概括
超大复合物库提供了新的药物发现潜力. 一个进化算法,RosettaEvolutionaryLigand (REvoLd),有效地搜索这些庞大的化学空间,大大提高了药物目标的命中率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 超大型的按需制作 (LOD) 化合物库包含数十亿种化合物.
- 由于接收器的灵活性,通过计算选这些库是耗时的.
- 在体中发现药物需要对广的化学空间进行高效的探索.
研究的目的:
- 开发一种高效的算法,用于探索超大型按需制造的化学库.
- 为了解决大复合库的选与受体灵活性的计算成本.
- 为了优化在广的组合化学空间中的联结蛋白对接.
主要方法:
- 开发了RosettaEvolutionaryLigand (REvoLd),这是一个进化算法.
- 使用了按需制作库的功能 (基板和反应).
- 集成REvoLd与RosettaLigand,以实现完全灵活的蛋白质 - 合物对接.
主要成果:
- REvoLd有效地搜索组合化学空间,而无需进行完整的列举.
- 对五种药物目标的基准测试显示,与随机选择相比,命中率提高了869至1622倍.
- 在药物发现查中表现出强大而稳定的丰富性.
结论:
- REvoLd为探索超大化学空间提供了迄今为止最有效的算法.
- 进化方法优化了在体中发现药物的探索.
- REvoLd可以作为Rosetta软件套件中的一个应用程序.
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