论文标题
通过交互知识传递，减轻药物目标亲和力预测中的冷启动问题
Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring
论文作者
论文摘要
动机：预测药物目标相互作用对于药物发现和药物重新利用至关重要。机器学习通常用于药物目标亲和力（DTA）问题。但是，机器学习模型面临着一个冷启动的问题，在预测新型药物或目标的相互作用时，模型性能会下降。以前的工作试图通过使用无监督学习来学习药物或目标表示来解决冷启动问题。尽管可以以无监督的方式学习药物或靶标表示，但它仍然缺乏相互作用信息，这对于药物靶标相互作用至关重要。结果：将相互作用信息纳入药物和蛋白质相互作用中，我们提出了使用化学化学相互作用（CCI）和蛋白质 - 蛋白质相互作用（PPI）任务的转移学习到药物靶标相互作用任务。由于任务的性质相似，因此CCI和PPI任务学到的表示可以顺利传输到DTA任务。药物目标亲和力数据集的结果表明，与DTA任务中的其他训练方法相比，我们提出的方法具有优势。
Motivation: Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. Results: To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the drug-target affinity datasets shows that our proposed method has advantages compared to other pretraining methods in the DTA task.
