Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods.

中文翻译：

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Metapath2vec 节点嵌入异构网络的药物-靶点相互作用预测

药物发现是一个复杂、耗时且昂贵的过程。每个新分子实体 (NME) 的成本估计为 18 亿美元。此外，一种新药要获得 FDA 批准通常需要近十年的时间，美国食品和药物管理局 (FDA) 每年批准大约 20 种新药。通过计算方法准确预测药物-靶点相互作用（DTI）是药物研究的一个重要领域，为快速、低风险的药物开发带来了广阔的前景。通过准确预测药物和靶点的相互作用，科学家可以缩小巨大的实验空间，降低成本，帮助加快药物开发，预测新药的副作用和潜在功能。研究人员已经采取了许多方法来解决 DTI 问题并提高方法的准确性。最先进的方法基于各种技术，例如深度学习方法，如堆叠自动编码器、矩阵分解、网络推理和集成方法。在这项工作中，我们采用了一种基于节点嵌入在异构交互网络中的新方法，以获取交互网络中每个节点的表示，然后使用逻辑回归等二元分类器来解决制药行业的这一突出问题。大多数引入的基于网络的方法都使用同质的交互网络作为输入数据，而在实际问题中存在其他信息网络来帮助增强预测，并且通过考虑同质网络，我们会丢失一些宝贵的网络信息。因此，在这项工作中，我们尝试在异构网络上工作，并与基线方法相比提高了方法的准确性。