FAME3R:一种高效,实用和可靠的开源工具,用于预测代谢第一阶段和第二阶段的部位
Roxane Axel Jacob1,2,3, Leo Gaskin1, Thomas Seidel1,2
1Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090, Vienna, Austria.
Journal of cheminformatics
|February 14, 2026
概括
FAME3R增强了FAME3工具,用于预测分子中的代谢部位 (SOMs). 这种改进的模型为药物发现和化学开发提供了更好的计算效率和集成.
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:
- 预测代谢部位 (SOMs) 对于开发制药,农业化学品和化品至关重要.
- 作为一期和二期SOM预测的非商业工具,FAME3模型在可用性和集成方面遇到了局限性.
- 这些局限性阻碍了FAME3在计算开发管道中的更广泛采用.
研究的目的:
- 增强FAME3模型以提高计算效率和集成到现代化工信息学工作流程中.
- 解决原始FAME3实现的局限性,包括可维护性,可扩展性和互操作性.
- 为预测各种异生菌代谢部位提供灵活和易于使用的工具.
主要方法:
- 开发FAME3R,这是FAME3 SOM预测工具的升级版.
- 实施一种使用香农的新型可靠性评估方法.
- 引入可选择的特色化策略来定制预测.
- 创建一个开源的Python包,包含Python API和命令行接口 (CLI).
主要成果:
- 与原来的FAME3模型相比,FAME3R显示了更好的计算效率.
- 新的可靠性评估方法提高了对预测SOM的信心.
- 通过可选择的特色化策略,FAME3R提供了更大的灵活性.
- 开源包和Web平台访问 (GUI,REST API) 增加了可访问性和互操作性.
结论:
- FAME3R克服了FAME3的局限性,为SOM预测提供了一个更易于维护,可扩展和可互操作的解决方案.
- 改进后的工具有助于无整合到当代化学信息学和计算药物发现工作流程中.
- FAME3R为制药,农业化学品和化品研究人员提供了一个有价值的开源资源.
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