Molecular Scaffold

Molecular scaffolds, the core structures around which molecules are built, are central to drug discovery and protein design. Current research focuses on improving the design and prediction of scaffolds using various machine learning models, including diffusion models, graph neural networks, and reinforcement learning algorithms, often incorporating techniques like motif scaffolding and fragment-based approaches. These advancements aim to enhance the efficiency and accuracy of virtual screening, protein engineering, and molecular property prediction, ultimately accelerating the development of new therapeutics and materials. The development of more realistic data splitting methods for model evaluation is also a key area of focus.