Enhancing drug synergy in malignant diseases with deep architecture optimization algorithms.

Malignant diseases remain one of the leading causes of death globally. Drug synergy has emerged as an effective approach for treating malignancy, offering improved therapeutic outcomes. Although techniques like clinical trials and high-throughput drug screening are commonly used to discover promising synergistic drug pairs, but they are time consuming and expensive. With the evolution of artificial intelligence, deep learning models are increasingly being applied to identify synergistic drug combinations. These models rely heavily on large-scale datasets, where size of dataset and hyperparameter selection play a pivotal role in the performance of model. However, determining the optimal hyperparameters for drug synergy models is a complex and time-intensive task, typically involving multiple iterative experiments. As a result, there is growing attention on optimizing hyperparameters for these models. This study emphasizes the role of hyperparameter optimization algorithms (HOAs) in evaluating how different optimization strategies and hyperparameter choices influence model effectiveness. By optimizing the hyperparameters, we achieved good accuracy in predicting drug synergy. Our findings highlight that the effectiveness of hyperparameter optimization is highly task- and dataset-dependent.