MT-EpiPred:用于预测小分子表观遗传调节器的多任务学习
Ruihan Zhang1, Xingran Xie1, Dongxuan Ni1
1Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education; Yunnan Key Laboratory of Research and Development for Natural Products; The Cloud Computing Engineering Research Center of Yunnan Province; Key Laboratory of Software Engineering of Yunnan Province; School of Software; School of Pharmacy, Yunnan University, Kunming 650500, P. R. China.
Journal of chemical information and modeling
|December 18, 2023
概括
MT-EpiPred预测了78个表观遗传点的复合活性,优于现有的方法. 这种多任务学习工具有助于发现新型表观遗传调节器,并了解它们对整个网络的影响.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 表观遗传调节器对于疾病治疗至关重要.
- 了解它们对整个网络的影响至关重要,而不仅仅是个别目标.
研究的目的:
- 介绍MT-EpiPred,一种多任务学习方法,用于对78个表观遗传标预测化合物活性.
- 评估MT-EpiPred的性能,并将其与现有方法进行比较.
主要方法:
- 开发了一种多任务学习模型MT-EpiPred.
- 在化合物和表观遗传标的数据集上训练并验证了模型.
- 应用MT-EpiPred来预测新型化合物的标,并验证了体外发现.
主要成果:
- MT-EpiPred实现了0.915的平均auROC.
- 该模型表现出处理少数射击目标的熟练程度.
- 确定KDM4D作为一种新型化合物的潜在标,在体外验证 (IC50 = 4.8 μM).
结论:
- MT-EpiPred提供了优越的预测性能和比现有方法更广泛的目标范围.
- 网络服务器为发现表观遗传调节器提供了可访问和准确的工具.
- 促进选择性抑制剂的开发和网络层面的影响评估.
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