Deep Artificial Neural Network Regression Model for Synergistic Drug Combination Prediction

The use of drug-drug interaction or drug combinations has become increasingly important for treating complex diseases but selecting the right combination can be challenging. Therefore, many computational methods proposed to predict drug interactions that can be used before clinical experiments and reduce the risk of adverse effects during treatment. in this paper, we proposed a deep artificial neural network regression (DANNR) model that can measure the strength of a combination between two drugs by predicting the synergy score. It is capable of being trained on high-throughput screening data to forecast how new drug combinations may affect cancer cell lines and patients. Findings from the study revealed that the proposed model is able to predict the synergistic effects of drug combinations with high accuracy. The model has been shown to be highly accurate in predicting the synergy between different drug combinations making it a valuable tool for the research and discovery of drugs.