FIGURE 9.

(i) An illustration showing the working of CLM on the basic framework of LLM; (ii) Principles of chemical language models (CLMs). (a) Example of a molecular structure (Kekulé structure) and a corresponding SMILES string. (b) CLMs are trained to iteratively predict the next SMILES character based on the preceding string characters. (c) Multinomial sampling can be used to generate new SMILES strings from trained CLMs, where SMILES characters are sampled with a weighted random sampling of probability distributions learned by the CLM.