Protein Target

Protein target identification and ligand design are crucial for drug discovery, aiming to find molecules that effectively bind to specific proteins involved in disease. Current research heavily utilizes machine learning, employing diverse models like graph neural networks, transformers, variational autoencoders, and diffusion models to predict protein-ligand interactions, generate novel drug candidates, and optimize their properties. These computational approaches accelerate the drug discovery process, offering significant potential for developing more effective and targeted therapies by reducing the time and cost associated with traditional methods. The integration of multimodal data (e.g., protein sequences, 3D structures, chemical fingerprints) and human-in-the-loop approaches further enhances the efficiency and accuracy of these predictions.