Molecular Embeddings

Molecular embeddings represent chemical structures and properties as numerical vectors, enabling machine learning models to analyze and predict various molecular characteristics. Current research focuses on developing sophisticated embeddings using techniques like graph neural networks, large language models (LLMs), and multimodal approaches that integrate both chemical language and physicochemical features, often aiming for improved efficiency and accuracy in tasks such as similarity searching, property prediction, and drug discovery. These advancements are significantly impacting drug discovery and materials science by accelerating processes like virtual screening and enabling more efficient analysis of large chemical databases. The development of more informative and computationally efficient embeddings remains a key area of ongoing investigation.