Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility. Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

中文翻译：

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基于元学习的归纳逻辑矩阵完成用于激酶抑制剂的预测

蛋白激酶成为潜在药物靶点的重要来源。开发新型、高效、安全的小分子激酶抑制剂已成为药物研发领域的重要课题。与耗时且昂贵的传统湿实验相比，基于机器学习的预测蛋白激酶小分子抑制剂的方法既节省时间又具有成本效益，这是我们非常希望的。然而，样本稀缺的问题（已知的活性和非活性化合物通常对大多数激酶来说是有限的）对基于机器学习的激酶抑制剂的活性预测方法的研发提出了挑战。为了缓解激酶抑制剂预测中的数据稀缺问题，在本研究中，我们提出了一种新颖的基于元学习的归纳逻辑矩阵完成方法用于激酶抑制剂的预测（MetaILMC）。 MetaILMC采用元学习框架，从样本充足的任务中学习泛化良好的模型，可以快速适应样本有限的新任务。由于 MetaILMC 允许将从具有足够样本的激酶中学到的先验知识有效转移到具有少量样本的激酶，因此所提出的模型可以对有限数据的激酶进行准确的预测。实验结果表明，MetaILMC 在少样本样本的激酶预测任务中具有优异的性能，并且在 AUC、AUPR 等各种性能指标方面显着优于最先进的多任务学习。案例研究还提供了两种药物来预测激酶抑制评分，进一步验证了所提出方法的有效性和可行性。考虑到不同激酶的活性预测任务之间的潜在相关性，我们提出了一种新的元学习算法MetaILMC，该算法在元训练期间从具有足够训练样本的任务中学习强泛化能力的先验，从而可以轻松快速地适应元测试期间数据稀缺的激酶的新任务。因此，MetaILMC可以有效缓解激酶抑制剂预测中的数据匮乏问题。