Solitarius: An Agentic Architecture for Computer-aided Molecular Inverse Design

Applying generative molecular design requires both chemical knowledge to turn a scientific aim into a computable objective and technical skill to configure and run the workflow that pursues it. Here, we present Solitarius , an architecture for automated computer-aided molecular discovery. It leverages agentic large language models (LLMs) for planning and configuring molecular generation workflows while delegating the underlying operations (e.g., model training, generating chemical structures, and optimisation) to a dedicated molecular generative AI platform. Solitarius further guides the LLM agents to use this platform as a human expert would, selecting appropriate procedures for a given objective and executing them accordingly. We first test whether LLM agents alone, without this guidance, are capable of reliably running the generative chemistry workflow, which exposes a gap in their ability to manage multi-step procedures. Equipped with both key generative algorithms and guidance, first, we apply Solitarius to the design of singlet-fission chromophores that satisfy the desired excited-state energetic criteria. We then use Solitarius to expand an organocatalyst dataset into sparsely populated regions of its property space. Across these examples, we demonstrate how Solitarius interprets scientific intent from plain-language goals, formulates scoring logic that reflects the desired objective, and iteratively executes molecular-generation workflows until the desired objectives are met. In the singlet-fission design example, Solitarius revises the planned design workflow as the search strategy evolves with the new results, shifting from open-ended de novo generation to structure-guided exploration using filters to avoid rediscovery and focus on the vicinity of the input molecules. For dataset expansion, Solitarius independently analyses the dataset to identify sparse regions. It then formulates an approach to populate them while preserving key catalytic structural motifs.