机器学习框架集成基于基和基于目标的算法,用于阐明药物目标
Haojie Wang1, Haotian Gao1, Zhe Zhang1
1Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
Journal of medicinal chemistry
|December 8, 2025
概括
我们开发了COMET,这是一种用于识别潜在分子标的计算药物发现工具. 这种方法集成了连接物和目标数据,在药物开发的准确性和速度上优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 确定生物活性化合物的分子标 (标捕捞) 在药物发现中至关重要.
- 计算方法加快了目标识别,减少了实验工作量.
- 现有的方法需要提高准确性和效率.
研究的目的:
- 开发一种名为COMET的新型计算目标捕捞方法.
- 整合基于连接体和基于目标的评分,以提高目标排名.
- 为药物发现研究人员提供一个用户友好和高效的工具.
主要方法:
- COMET使用一个随机森林算法,整合了连接体相似性和目标结合得分.
- 它利用2685个人类目标和990,944个连接物-目标相互作用的精心策划的数据集.
- 一个模块化框架允许未来的方法增强.
主要成果:
- 在对头比较中,COMET与其他七种方法相比表现优越.
- 在大型测试套件中,在前100名的回忆率和前15名的成功率中达到70-80%.
- 展示了计算速度和预测准确性之间的最佳平衡.
结论:
- COMET是一个高精度和高效的计算工具,用于药物发现目标捕鱼.
- 它的性能和速度使其适合于制药研究中的实际应用.
- 该工具可用于免费试用,促进更广泛的采用和验证.
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