数据缩放和概括 医学化学的洞察力 深度学习模型
Jacky Chen1, Yunsie Chung1, Jonathan Tynan1
1Modeling & Informatics, Merck & Co., Inc., South San Francisco, California 94080, United States.
深度学习模型,特别是图形神经网络,在小分子药物发现预测方面优于传统的机器学习. 一个新的缩放关系准确地估计了在各种测试和数据条件中模型的性能.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 药理学 药理学是指药理学的学科.
背景情况:
- 预测模型加速发现更安全,更有效的治疗方法.
- 了解和提高小分子预测模型的性能对于药物发现至关重要.
- 使用深度学习和传统的机器学习方法.
研究的目的:
- 为了比较深度学习和传统机器学习模型的性能,用于小分子药物发现.
- 识别导致模型性能差异的因素.
- 为模型性能开发一个预测性缩放关系.
主要方法:
- 使用深度学习 (图形神经网络) 和传统机器学习 (XGBoost,随机森林) 的实验.
- 利用大型内部和公共数据集.
- 评估随机,时间和反时间数据消去任务以及外推任务上的模型性能.
主要成果:
- 与传统方法相比,图形神经网络显示出更高的性能.
- 一个开发的缩放关系解释了81%的模型性能在各种测试和数据制度的差异.
- 确定了影响模型性能的关键因素.
结论:
- 深度学习,特别是图形神经网络,为药物发现中的小分子预测建模提供了显著的优势.
- 已建立的缩放关系为估计模型性能和指导未来开发提供了有价值的工具.
- 结果为改善药物发现管道中的预测模型有效性提供了实际指导.
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