使用密度矩阵碎片化和基于物理的机器学习分散潜力,准确和快速排名蛋白质 - 连接物结合的亲缘关系
1Department of Chemistry, Virginia Commonwealth University, Richmond, 23284, VA, USA.
概括
密度矩阵 (GMBE-DM) 的通用化多体扩展和机器学习校正的分散潜力 (D3-ML) 准确地排列蛋白质-联体结合亲缘关系. 在药物发现查中,D3-ML表现出了卓越的速度和准确性.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 准确地预测蛋白质 - 配体结合亲缘关系对于药物发现至关重要.
- 现有的方法经常面临效率,准确性或在各种化学系统中可转移性方面的挑战.
研究的目的:
- 评估用于构建密度矩阵 (GMBE-DM) 的通用化多体扩张的性能,以及用于排名蛋白质-联体结合亲缘关系的物理信息,机器学习校正的分散潜力 (D3-ML).
- 在准确性,效率和可转移性方面,将这些方法与深度学习模型 (Sfcnn) 进行比较.
主要方法:
- 应用GMBE-DM方法,在单体水平上使用净化方案进行切断.
- 开发和应用D3-ML潜力,结合机器学习纠正分散相互作用.
- 对循环素依赖激酶2 (CDK2) 和雅努斯激酶1 (JAK1) 的数据集进行测试,共计28个配体.
主要成果:
- GMBE-DM与实验性的结合自由能量 (R2 =0.84) 实现了强烈的相关性,具有高效的运行时间 (每组合<5分钟).
- D3-ML表现出优越的排名性能 (R2 =0.87),运行时间小于秒,突出了分散相互作用的重要性.
- 深度学习模型Sfcnn在数据集之间具有较低的可转移性 (R 2 = 0.57).
结论:
- GMBE-DM和D3-ML是强大的和可扩展的计算工具,用于排名蛋白质-连接体结合亲缘关系.
- D3-ML提供了特殊的速度和准确性,使其非常适合用于药物发现中的高通量虚拟查.
- 这项研究强调了分散相互作用的关键作用以及各种化学系统中广泛训练的神经网络的局限性.
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