Large Scale Screening

Large-scale screening uses computational and experimental methods to rapidly analyze vast datasets, aiming to identify promising candidates from diverse fields like drug discovery and materials science. Current research emphasizes machine learning, particularly graph neural networks and deep learning models, often incorporating techniques like active learning and self-supervised learning to improve efficiency and accuracy. This approach significantly accelerates the discovery process across various scientific domains, enabling faster identification of novel therapeutics, catalysts, and diagnostic tools while reducing the cost and time associated with traditional methods.