A Novel Approach in Automatic Identification of Cancer Cell Drug Sensitivity Utilizing Regression-Based Ensemble Convolution Neural Networks

In the modern medical advancements, the neural drug designing and sensitivity prognosis put a pace forward to novel methodologies and focused to reach the goal of predicting anti-cancer compound sensitivity by implementing multimodal-based convolution encoder. This novel approach is executed in three major key moves: established knowledge of intracellular interactions derived from protein-protein interaction networks, gene expression profiles from tumors, and the chemical structures represented as SMILES sequences. Our multi-scale convolutional attention-based encoder achieves an R2 value of 0.86 and an RMSE of 0.89, significantly surpassing a baseline model that utilizes Morgan fingerprints, various SMILES-based encoders, and the previously recognized state-of-the-art methods for multimodal drug sensitivity prediction. In addition, we introduce the Ensemble Convolution Neural Network Model: A Novel Regression-Based Approach (ECNN-NRNN) for drug sensitivity analysis, which leverages multiple pharmacogenomic datasets while addressing the heterogeneity in feature selection for sub-pharma comic parameters. Given that certain pharmacogenomic data is accessible online and should be made publicly available, it is crucial to focus on drug sensitivity prediction as well as drug identification and design. Enhancements in sensitivity prediction can be achieved through conventional methods, and we will provide an experimental evaluation to demonstrate these improvements.