AI-Driven Drug Screening

The integration of Artificial Intelligence (AI) into drug discovery has redefined the traditional image of pharmaceutics, aiding in reducing price and better prognosticative accuracy. AI, through Machine Learning (ML) and Deep Learning (DL), addresses this limitation of traditional methods by enabling rapid virtual screening, information-driven decision-making, and intelligent prioritization of compounds. Techniques such as molecular histrionics utilize SMILES, signifier, fingermark, and graph-establish networks heighten the prognostication of biological natural processes. AI correlates chemic, genomic, and clinical data to assist in target recognition, drug repurposing, and personalized drug development. However, challenges such as circumscribed data point availability, the dark-corner nature of models, and the experimental validation crack remain critical barriers. Emerging approaches like Explainable AI (XAI), Physics-Informed Machine Learning (PIML), etc. promise to heighten model transparency and reliability.