Molecular Representation

Molecular representation focuses on encoding the complex information within molecules into formats suitable for machine learning, aiming to improve predictions of molecular properties and facilitate drug discovery and materials science. Current research emphasizes multimodal approaches, integrating various data types like molecular graphs, SMILES strings, and textual descriptions, often leveraging graph neural networks (GNNs), transformers, and contrastive learning methods. These advancements enable more accurate and efficient prediction of molecular properties, accelerating the design and development of new molecules with desired characteristics. The resulting improvements in molecular understanding have significant implications for diverse fields, including drug discovery, materials science, and environmental chemistry.