Current status and challenges of artificial intelligence-enabled prediction of synergistic cancer drug combinations

In recent years, the incidence and mortality rates of cancer have been rising steadily, with drug resistance becoming a major challenge in cancer treatment. Traditional monolithic treatment approaches have proven ineffective in addressing the heterogeneity of tumor cells and their multiple resistance mechanisms, leading to suboptimal therapeutic outcomes. Drug combination therapy, as a critical strategy, aims to enhance efficacy and delay the development of drug resistance through the synergistic action of multiple drugs. However, conventional methods for screening drug combinations are time-consuming and costly. With the accumulation of data and advances in computational methods, artificial intelligence, particularly deep learning, has demonstrated great potential in predicting synergistic drug combinations for cancer treatment. Artificial intelligence technologies allow researchers to efficiently predict whether drug combinations exhibit synergistic effects, reducing experimental costs and identifying novel potential synergistic combinations. Nevertheless, artificial intelligence models still face challenges such as poor interpretability, insufficient feature integration, and a lack of labeled data. This paper provided a comprehensive review of the advancements in artificial intelligence applications for predicting synergistic drug combinations in cancer therapy. First, it discussed the mechanisms of drug resistance and the challenges of combination therapy, highlighting the limitations of traditional drug combination screening methods. Then, it presented the advantages and disadvantages of various deep learning models used for predicting synergistic drug combinations, including feedforward neural networks, graph neural networks, autoencoders, visible neural networks, Transformer and their extended models. In response to the common issues in current deep learning models, this review proposed several solutions, such as leveraging multimodal data to enhance model generalization, employing transfer learning and multitask learning to address data scarcity, and designing more interpretable models to facilitate clinical application. In the future, the field of synergistic drug combination prediction is expected to benefit from the development of standardized synergy metrics, improvements in model interpretability, integration of multimodal data, and effective handling of data limitations, further advancing the transition of models from laboratory research to clinical practice, ultimately providing more effective solutions for cancer treatment.