通过结合-蛋白对接和机器学习来加速小配体的选
Josep-Ramon Codina1, Marcello Mascini2, Emre Dikici1,3
1Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.
International journal of molecular sciences
|August 12, 2023
概括
这项研究引入了一种机器学习 (ML) 管道,用于快速预测蛋白对接. 光渐变增强机 (LightGBM) 模型加速了小配体选,有效地识别了潜在的生物活性化合物.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 蛋白对接对于药物发现至关重要,但计算密集.
- 加快小联体选需要高效的计算方法.
- 现有的方法往往需要大量的高性能计算资源.
研究的目的:
- 开发和验证一种新的管道合机器学习 (ML) 和分子对接,用于加速-蛋白对接预测.
- 评估各种ML算法的性能,用于分类蛋白对接结果.
- 为了证明拟议的ML驱动的小配体选方法的计算效率和准确性.
主要方法:
- 八个机器学习算法被评估为蛋白对接预测.
- 光渐变增强机 (LightGBM) 是因为其计算效率而被选中.
- 使用ML和分子对接,对16万个四白连接体的库进行了对抗四种病毒包膜蛋白的选.
- 机器学习模型是对1%的数据进行训练,并用于分类剩余的99%.
主要成果:
- 与其他ML算法相比,光梯度增强机 (LightGBM) 显示出更高的计算效率.
- 经过训练的LightGBM模型在99%的数据上实现了0.81-0.85的准确性和0.58-0.67的F1得分,用于分类蛋白对接性能.
- 与ML合的分子对接管道实现了与传统方法相匹配的90-95%,同时加速了至少10倍的过程.
- 这种方法被证明是独立于使用的特定分子对接软件.
结论:
- 机器学习与分子对接相结合,为加速小联体选提供了一种有效的策略.
- 开发的管道有效地识别了高性能,而不需要高性能计算.
- 这种方法为快速识别药物发现中的潜在生物活性化合物提供了有价值的工具.
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