转移学习用于药物向相互作用预测
Alperen Dalkıran1,2, Ahmet Atakan1,3, Ahmet S Rifaioğlu4,5
1Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
深度转移学习有效地预测药物向相互作用的研究不足的蛋白质与有限的数据. 这种人工智能方法在训练数据集小时优于传统方法,加速药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 人工智能在药物发现中的作用
- 生物信息学是一种生物信息学.
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物发现至关重要.
- 人工智能驱动的DTI预测方法需要大量的训练数据,这对于研究不足的蛋白质通常是不可用的.
- 深度转移学习为使用有限数据进行DTI预测提供了一个潜在的解决方案.
研究的目的:
- 调查深度转移学习对预测药物向相互作用 (DTI) 的有效性,涉及缺乏训练数据的研究不足的蛋白质.
- 为了评估与从头开始训练深度神经网络相比转移学习的性能,用于DTI预测.
主要方法:
- 一个深度神经网络分类器在一个大,通用的源数据集上进行了预训练.
- 预先训练的网络使用较小的,专门的目标数据集进行了微调,用于研究不足的蛋白质家族 (例如,载体,核受体).
- 在不同转移学习策略中系统评估绩效,并与从头开始的传统培训进行比较.
主要成果:
- 当目标训练数据集包含不到100个化合物时,深度转移学习显著超过了从头开始的训练.
- 这项研究表明,转移学习的优势是预测药物结合剂对未研究过的蛋白质标的作用.
- 该方法在生物医学中使用关键蛋白家族进行了验证,包括激酶,GPCR,离子通道,核受体,蛋白酶和载体.
结论:
- 深度转移学习是药物向相互作用预测的强大和有利的方法,特别是对于具有有限可用的培训数据的目标.
- 这种方法可以加速对未经研究的蛋白质的候选药物的鉴定,解决药物发现的关键瓶.
- 开发的模型和代码是公开的,这有助于进一步的研究和应用.
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