molLm

molLm is a term used in discussions of artificial intelligence that seeks to fuse molecular representations with large language modeling. It does not refer to a single standardized model, but rather to a family of approaches in which chemical structures and textual information are processed by a shared or jointly trained system. In this context, molecules are encoded as sequences of tokens (such as SMILES or SELFIES) or as graph representations that a language model can attend to.

Architectures vary from augmenting a pre-trained language model with a molecular encoder to multimodal transformers that jointly process graphs and text. Training data often combines chemical databases, scientific literature, and reaction datasets. Objectives commonly include property prediction, reaction outcome forecasting, and the generation or optimization of molecules, possibly accompanied by human-readable explanations or rationales in natural language.

Potential applications include predicting physical and biological properties, planning synthetic routes, and assisting in compound design. By integrating textual context with structural information, molLm aims to capture both quantitative signals and domain knowledge embedded in chemistry literature.

Challenges include data quality and standardization, interpretability, and safety considerations for designing potentially hazardous substances. Tokenization and representation choices affect generalization to novel chemistries, and computational demands can be high. As of the early 2020s, molLm is a research concept with multiple competing implementations rather than a single, widely adopted standard.