Innovative Methods for Synergistic Drug Combination Prediction Using Deep Learning Models

This research proposes utilizing modern computer technologies and deep learning models to predict how drugs may interact. The recommended technique records intricate biological correlations in medication interactions using GNNs, CNNs, and attention mechanisms. A thorough loss function determines the optimum prediction accuracy and regularization. This prevents the model from getting too accurate and ensures it works on all datasets. The method improves predictions by repeatedly increasing synergy ratings and adding feedback mechanisms. Performance experiments reveal that the recommended strategy outperforms existing methods in memory, accuracy, and precision. Advanced optimization methods, including dropout values and interaction variables, improve model predictions. The findings demonstrate that the technique may provide important treatment information, making medication combination therapy more effective in clinical settings. This paper improves computational drug development by demonstrating how to investigate potential medication combinations.