Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.

Authors

• Dilbag Singh
• Ahmad Ali AlZubi
• Manjit Kaur
• Vijay Kumar University of Pennsylvania
• Heung-No Lee

Keywords

• Drugs
• Predictive models
• Feature extraction
• Cancer
• Computer architecture
• Sensitivity
• Long short term memory
• Deep learning
• Deep Network
• Synergistic Drug
• Synergy Prediction
• Drug Synergy Prediction
• Learning Models
• Overfitting
• Drug Combinations
• Drug Interactions
• Deep Learning Models
• Tuning Parameter
• Connection Weights
• Vanishing Gradient
• Bidirectional Network
• Effective Drug Combinations
• Cancer Cells
• Cancer Cell Lines
• Model Performance
• Training Data
• Convolutional Neural Network
• Mean Absolute Percentage Error
• Graph Convolutional Network
• Competition Model
• Extreme Gradient Boosting
• Forget Gate
• Synergistic Effect Of The Combination
• Input Gate
• Deep Learning Techniques
• 3D Mesh
• Drug-target Prediction
• Drug synergy
• modified triangular mutation
• differential evolution
• cancer treatment
• Humans
• Drug Synergism
• Mutation
• Algorithms
• Neoplasms
• Computational Biology