An efficient drug synergy model based on deep learning for malignant diseases

Malignant diseases are the leading cause of mortality worldwide. Drug resistance is one of the significant reasons for mortality in malignant diseases. Chemical treatment involves the administration of many drugs, either all at once or in a specific order, intending to minimize the size of a tumor or eliminate it. With the combination of drugs or drug synergy, the therapeutic response of drugs is greater than the expected additive monotherapy. The identification of synergistic drugs with clinical trials is a time-consuming and expensive task. Due to a lack of available resources, it is impracticable to screen all of the essential synergistic drugs. The introduction of artificial intelligence (AI) has not only altered our perspective but also presented new approaches to old challenges. One area where significant progress has been made because to AI advancements is the prediction of drug synergy based on deep learning in the treatment of malignant diseases. In this paper, we provide a unique deep learning-based drug synergy model for malignant diseases. The proposed model is used to determine the most successful synergistic drug combinations, and it may also increase the practicality of exploring new drug combinations.