Molecular Data

Molecular data analysis aims to leverage the vast amount of information encoded in molecular structures to predict properties and understand biological processes. Current research heavily utilizes graph neural networks (GNNs), transformers, and other deep learning architectures to represent molecules, often incorporating multiple data modalities (e.g., 2D graphs, 3D coordinates, SMILES strings) for improved accuracy. This field is crucial for accelerating drug discovery, materials science, and other areas by enabling faster and more accurate prediction of molecular properties, reducing reliance on expensive and time-consuming experiments. The development of robust and generalizable models, particularly those addressing data scarcity and imbalanced datasets, remains a key focus.