使用内部坐标和生成方向图形卷积神经网络有效生成符合组合
Eugene Raush1, Ruben Abagyan2, Maxim Totrov1
1Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, California 92121, United States.
Journal of chemical theory and computation
|April 26, 2024
概括
我们开发了GINGER,这是一个神经网络算法,用于快速生成分子适应器. 这种方法有效地从二维化学图表中预测低能量的3D分子结构,加速药物发现.
科学领域:
- 计算化学的计算化学
- 化学中的人工智能.
- 分子建模分子建模
背景情况:
- 准确预测分子3D结构对于理解化学和生物过程至关重要.
- 传统的适应器生成方法在计算上可能是昂贵和缓慢的,这限制了它们对大数据集的应用.
- 开发高吞吐量和精确的算法用于分子调整器生成是计算化学的一个持续挑战.
研究的目的:
- 介绍一种基于神经网络的新算法,用于高通量分子适应器生成.
- 通过使用内部坐标表示,从二维化学拓中预测低能变态.
- 提供一种计算效率高,准确的方法来生成符合组合.
主要方法:
- 一个化学图形-卷积网络被训练来预测使用内部坐标 (键长,键和扭转角度) 的低能变态.
- 一个生成的神经网络架构被用于扭力空间中的denoising,生成与扭力能量配置文件相关的符合组合.
- 基于力场的能量最小化被用来改进生成的对应器,并对计算密集的阶段进行GPU优化.
主要成果:
- 在标准 PDB 测试集上进行基准测试时,GINGER 算法在符合性回收方面表现出高度竞争力的结果.
- 该方法实现了高吞吐率,适合处理大规模的复合库 (千兆级).
- 由GINGER生成的调节器组合显示,种群与扭力能量概况有很好的相关性.
结论:
- GINGER算法提供了一种高效和准确的方法,用于高通量分子适应器生成.
- 这种方法显著提高了处理大型化学库的能力,用于药物发现和分子建模.
- 有一个用于交互式调节器生成和查看的Web服务器,可方便更广泛的访问和应用.
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