Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

翻译：人工智能（AI）以及深度与图学习模型的最新进展和成就，已证明它们在生物医学应用中的实用性，尤其是在药物-药物相互作用（DDIs）领域。DDIs指一种药物因人体内存在另一种药物而改变药效的现象，在药物发现和临床研究中至关重要。通过传统临床试验和实验进行DDIs预测是一个昂贵且耗时的过程。为了正确应用先进AI和深度学习技术，开发者和用户面临诸多挑战，例如数据资源的可用性与编码，以及计算方法的设计。本综述总结了基于化学结构、基于网络、基于自然语言处理（NLP）及混合方法的DDIs预测方法，为具有不同领域知识的广大研究者和开发社区提供了一份更新的、易于理解的指南。我们介绍了广泛使用的分子表征方法，并阐述了用于分子结构表示的图神经网络模型的理论框架。通过对比实验，我们分析了深度与图学习方法的优缺点。最后，我们讨论了潜在的技术挑战，并指出了加速DDIs预测的深度与图学习模型未来发展方向。