AI - Driven Drug Discovery and Therapeutic Target Identification for Rare Genetic Diseases

This method for AI-driven drug discovery and therapeutic target selection for rare genetic diseases is a cutting-edge and comprehensive strategy for addressing the challenges posed by such diseases. As a prerequisite, it is necessary to collect and combine genomic, transcriptomic, proteomic, and clinical data from numerous sources in a systematic manner. By integrating genetic and clinical data, it is possible to construct a more complete picture of the disorders. Feature selection procedures are utilized to further simplify the research by focusing on the most essential characteristics for target identification and reducing the dimensionality of the data. Based on the integrated data set, machine learning models such as Neural Networks, Support Vector Machines, and Random Forests are utilized to recommend prospective therapeutic targets. Validation and cross-validation techniques are used to assure the validity and generalizability of these models. The strategy emphasizes adherence to applicable ethical principles and legal frameworks while encouraging collaboration across specialties. The patient is the focal point throughout, and new data is continuously collected to improve results and develop more individualized therapies based on everyone's unique genetic profile. The iterative nature of the methodology allows for continual refinement as new data and clinical insights emerge. AI-predicted targets undergo biological validation through in vitro and in vivo experiments, bridging the gap between computational predictions and clinical applications. Knowledge and insights are shared with the scientific community through open-access databases and collaborative platforms.