Molecule Translation

Molecule translation focuses on using artificial intelligence, particularly large language models (LLMs), to bridge the gap between textual descriptions and molecular representations (e.g., SMILES strings, 2D graphs, images). Current research emphasizes multi-modal approaches, integrating various data types to improve LLMs' understanding and generation of molecules, often employing novel architectures like transformers adapted for this cross-modal task and leveraging techniques like contrastive learning and feedback alignment for improved training efficiency. This work holds significant potential for accelerating drug discovery, materials science, and other fields by automating tasks such as compound name recognition, reaction prediction, and retrosynthesis, ultimately enhancing scientific productivity.