AI-Powered Drug Discovery: Accelerating Precision Medicine with Deep Learning

AI-powered drug discovery is a key factor in promoting precision medicine as it helps overcome the challenges of traditional drug discovery methods. The latter are commonly time-consuming and cost-ineffective, with high-throughput screening, and structure-activity relationship modeling causing the high prevalence of false negatives and false positives, leading to large drop-out metrics. The proposed system addresses the identified problem by using deep learning approaches to improve the accuracy and relevance of the predictions on drug-target interaction. It develops a data processing and methodologies scheme supported by example datasets based on genomic, clinical, and chemical data, and includes data collection, data preparation, model design, and model validation stages. The model performed with the use of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) demonstrates the benefits of the approach in terms of performance indicators and obtain 92.7% accuracy, 90.3% precision, and 89.1% recall. These results prove that the use of AI-related approaches not only saves the time and effort of the drug discovery process but also adds to its validity and accuracy, enabling efficient therapeutic options.