We consider choosing a molecule as the basis for generation (Figure ) and picking a scaffold from the molecule, or otherwise fixing some part of the molecule. This will be used as a conditioning sequence for generation, and represents a potential use case for a generative LLM: pre-training and/or fine-tuning the LLM on a particular region of chemical space and using its generative capabilities to explore the chemical space around a known hit or lead compound in a drug discovery campaign. Molecules are then generated using the conditioning sequence with either top-k/nucleus sampling or beam search. We may be interested in generating new samples that have similar properties to our original molecule, or in generating samples that preserve a scaffold or substructure but otherwise have significantly different properties.