基于机器学习的数据分析方法,用于基于细胞的DNA编码库的选择
Rui Hou1,2, Chao Xie1, Yuhan Gui1
1Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China.
ACS omega
|June 5, 2023
概括
使用最大后期 (MAP) 估计的新机器学习方法有效地处理基于细胞的杂DNA编码库 (DEL) 选择数据. 这种方法可靠地识别真正的结合物和结构-活性关系,改善复杂的生物环境中的连接物发现.
科学领域:
- 生物化学 生物化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- DNA编码图书馆 (DEL) 技术对于药品中的配体发现至关重要.
- 传统的DEL选择使用纯化的蛋白质,但基于细胞的选择面临由于噪音数据和非特异性相互作用的挑战.
- 现有的否定方法对于基于细胞的DEL数据的适用性仍然不确定.
研究的目的:
- 引入和验证一种基于机器学习 (ML) 的新型方法来处理基于细胞的DEL选择数据集.
- 为了应对从杂,复杂的细胞表面目标选择中识别可靠命中的挑战.
- 使用概率框架量化杂的DEL数据中的不确定性.
主要方法:
- 开发一种机器学习方法,利用一个最大后期 (MAP) 损失估计函数.
- 将基于扩展连接指纹 (ECFP) 的回归模型应用于基于细胞的DEL数据集.
- 计算一个规范化的丰富度指标 (MAP丰富度) 以抑制异常值.
主要成果:
- 基于ML-MAP的方法成功地从基于噪声细胞的DEL数据中识别出真正的结合剂和可靠的结构-活性关系 (SAR).
- 通过抑制低置信度异常值,MAP丰富度指标有效地提高了信号与噪声比.
- 证明了处理复杂的基于细胞的DEL选择数据集的原则证明.
结论:
- 基于ML-MAP的方法为分析基于噪音细胞的DEL选择数据提供了强大的解决方案.
- 这种方法提高了复杂生物系统中命中识别和SAR分析的可靠性.
- 未来的工作重点是将这种方法应用于从基于细胞的DEL选择中发现新的配体.
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