The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Automated summary

Drug combination therapy is a common strategy for treating complex diseases. Efficient identification of appropriate drug combinations using computational methods is urgently needed due to the high cost of experimental screening. Recent studies have shown that deep learning algorithms have the flexibility to integrate multimodal data and achieve state-of-the-art performance, making deep-learning-based prediction of drug combinations an important tool in future drug discovery.