Deep learning for predicting synergistic drug combinations: State-of-the-arts and future directions

Combination therapy has emerged as an efficacy strategy for treating complex diseases. Its potential to overcome drug resistance and minimize toxicity makes it highly desirable. However, the vast number of potential drug pairs presents a significant challenge, rendering exhaustive clinical testing impractical. In recent years, deep learning-based methods have emerged as promising tools for predicting synergistic drug combinations. This review aims to provide a comprehensive overview of applying diverse deep-learning architectures for drug combination prediction. This review commences by elucidating the quantitative measures employed to assess drug combination synergy. Subsequently, we delve into the various deep-learning methods currently employed for drug combination prediction. Finally, the review concludes by outlining the key challenges facing deep learning approaches and proposes potential challenges for future research. Combination therapy's efficacy: It's a vital strategy for treating complex diseases, overcoming drug resistance, and minimizing toxicity. Challenge of drug combination: The vast number of potential drug pairs makes exhaustive clinical testing impractical. Deep learning in drug combination prediction: Recent advancements use diverse deep learning architectures to predict synergistic drug combinations, addressing key challenges and suggesting future research directions. image