CoLiNN:一个工具快速化学空间可视化组合图书馆没有编号的工具
Regina Pikalyova1, Tagir Akhmetshin1, Dragos Horvath1
1Laboratoire de Chemoinformatique, University of Strasbourg, 4, rue B. Pascal, 67081, Strasbourg, France.
Molecular informatics
|March 18, 2025
概括
我们开发了结合式图书馆神经网络 (CoLiNN) 来预测化学空间地图,而无需复合计数. 这个工具通过分析构建块和反应,有效地设计组合式复合体库.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 药物发现 药物发现
背景情况:
- 化学空间可视化对于药物发现至关重要,但传统方法需要广泛的化合物计数.
- 现有的方法耗费大量时间和资源,阻碍了有效的图书馆设计.
研究的目的:
- 引入组合图书馆神经网络 (CoLiNN),这是一个用于预测二维化学空间地图中的化合物位置的新方法.
- 为了绕过化学空间可视化中复合物计数的需要.
主要方法:
- CoLiNN 在虚拟DNA编码图书馆 (DEL) 上接受了培训.
- 该模型使用构建块和反应数据预测生成地图 (GTM) 上的复合投影.
- 通过CoLiNN预测的GTM与从编号结构和ChEMBL数据库中生成的GTM进行了比较.
主要成果:
- CoLiNN展示了高预测性能,准确地将化合物映射到GTM上.
- 由CoLiNN生成的GTM非常类似于从列举的化合物中获得的地图.
- 图书馆比较任务显示了CoLiNN预测和真实GTM之间的一致排名,验证了其有效性.
结论:
- CoLiNN为组合式复合库设计提供了一种更有效的方法.
- 通过消除复合计数,CoLiNN加速了化学图书馆设计空间的探索.
- CoLiNN有可能成为优化药物发现库设计的重要工具.
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