Deep Learning-Driven Drug Development by Identifying Human Protein Targets

The advent of artificial intelligence with various optimization techniques has revolutionized the industry of drug development by providing an accurate prediction of protein targets for the development of therapeutic compounds. The proposed system provides an elaborative concept combining deep learning with optimization techniques for the recognition of protein targets for drug development. The first stage involves the collection of datasets that include various chemical compounds that are pre-processed to maintain consistency and stability in the system. The chemical structures are represented using molecular fingerprints. The interrelationships and the complex patterns between the chemical structures and the protein targets are observed using deep learning models such as Convolutional Neural Network (CNN) and Graph Neural Network (GNN). The optimization techniques are involved to maintain the performance of the proposed model. The optimal configuration of the system model and its parameters is identified using the Bayesian optimization technique. The model is trained on a pre-trained compound protein dataset which helps to extract the intricate mapping between chemical structures with associated target proteins. The minimization of the loss function by monitoring the convergence using a validation dataset is achieved in the training process. The predictive accuracy of the model is obtained through rigorous evaluation using separate test datasets. The performance of the model is observed through certain metrics such as accuracy, precision, recall, F1 score and ROC curves. Thus the proposed system provides optimal results in accurate recognition of human protein targets for the development of drugs. This leads to a time-consuming process compared with traditional methods in drug development. This helps to enhance the development of novel drugs for various diseases.