深度主动学习具有高的结构性可分辨性,用于分子突变性预测.
Huiyan Xu1,2, Yanpeng Zhao2, Yixin Zhang2
1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China.
Communications biology
|August 31, 2024
概括
预测突变性对于药物安全至关重要. 一个新的主动学习框架,muTOX-AL,有效地识别了测试的关键分子,大大降低了成本,提高了药物发现的准确性.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 药物发现 药物发现
背景情况:
- 突变性评估在药物发现中至关重要,以防止癌症和生殖细胞损伤.
- 在 silico 变异性预测受到有限的标记分子数据的阻碍.
- 实验测试是昂贵和耗时的,需要具有成本效益的注释策略.
研究的目的:
- 引入muTOX-AL,这是一个深度主动学习框架,用于有效预测突变性.
- 为了降低药物发现中分子注释的成本.
- 为了提高具有有限数据的 in silico 突变性预测模型的性能.
主要方法:
- 开发一个深度主动学习框架 (muTOX-AL).
- 积极探索化学空间以识别有价值的分子进行注释.
- 使用预言者 (例如,人类专家) 进行向的分子标签.
主要成果:
- muTOX-AL通过少量标记样本实现了竞争性性能.
- 与随机抽样相比,所需的训练分子数量减少了大约57%.
- 证明具有优越的选择具有高度结构相似性但具有不同的突变性质的分子的能力.
结论:
- muTOX-AL提供了一种有效的解决方案,用于药物发现中的突变性评估.
- 该框架显著降低了注释成本,同时保持了高预测性能.
- muTOX-AL的结构可分辨性有助于识别用于毒性预测的关键分子特征.
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