Chemical Language

Chemical language processing (CLP) leverages machine learning, particularly transformer-based architectures, to analyze and generate molecular representations like SMILES strings, enabling faster and more efficient drug and material discovery. Current research focuses on developing large-scale foundation models pre-trained on massive datasets of molecular structures, improving tokenization strategies for broader chemical coverage, and integrating multimodal data (e.g., physicochemical properties) to enhance predictive accuracy. These advancements are significantly impacting cheminformatics by accelerating property prediction, molecule generation, and ultimately, the design of novel molecules with desired characteristics.