3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation

Abstract

INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system.
 OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures.
 METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures.
 RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE) and area under the receiver operating characteristic curve (AUC-ROC).
 CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.