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编码DNA图书馆的组成深度概率模型
Benson Chen1, Mohammad M Sultan1, Theofanis Karaletsos1
1Insitro, South San Francisco, California 94080, United States.
Journal of chemical information and modeling
|February 9, 2024
概括
用DNA编码的库 (DEL) 产生复杂的数据. 一个新的深度学习模型,DEL-Compose,通过将分子分解成构建块来分析这些数据,改善药物发现洞察力.
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
- 生物信息学是一种生物信息学.
背景情况:
- DNA编码图书馆 (DEL) 是用于选小分子的强大工具.
- 德尔实验产生复杂的数据,可以掩盖重要的信号.
- 需要机器学习来从复杂的DEL数据中提取见解.
研究的目的:
- 介绍DEL-Compose,这是一个用于DEL数据分析的深度概率模型.
- 改进使用共变因子对DEL计数数据的观察模型.
- 提高DEL数据分析的可解释性和可靠性.
主要方法:
- 将分子表示分解为单合成,异合成和三合成的构建块.
- 使用层次结构模拟嵌入式synthons之间的潜在反应.
- 在观察模型中集成协变因子,以考虑数据噪声.
主要成果:
- 在公开基准数据集 (CA-IX和HRP) 上,DEL-Compose表现强.
- 该模型成功丰富了正确的药理,表明有效的结合剂识别.
- 本质的可解释结构为DEL数据提供了有价值的见解.
结论:
- DEL-Compose提供了一个强大的计算工具,用于分析复杂的DNA编码图书馆数据.
- 该模型的方法增强了强有力的结合剂的发现,并提供了可解释的结果.
- 这项工作推动了机器学习在基于DEL的药物发现中的应用.
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