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Logos, a compact molecular reasoning model, is introduced to address the limitations of existing models in molecular design by integrating multi-step logical reasoning with strict chemical consistency. It is trained using a staged strategy involving explicit reasoning examples, alignment with molecular representations, and incorporation of chemical rules into the optimization objective. Logos achieves strong performance in structural accuracy and chemical validity, surpassing larger language models while using fewer parameters, and demonstrates stable behavior in molecular optimization tasks with conflicting constraints.
By explicitly optimizing for both reasoning structure and chemical consistency, Logos offers a pathway to reliable and interpretable AI systems for molecular science, outperforming larger models with a fraction of the parameters.
The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design workflows.Here we present Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency. Logos is trained using a staged strategy that first exposes the model to explicit reasoning examples linking molecular descriptions to structural decisions, and then progressively aligns these reasoning patterns with molecular representations. In a final training phase, chemical rules and invariants are incorporated directly into the optimization objective, guiding the model toward chemically valid outputs. Across multiple benchmark datasets, Logos achieves strong performance in both structural accuracy and chemical validity, matching or surpassing substantially larger general-purpose language models while operating with a fraction of their parameters. Beyond benchmark evaluation, the model exhibits stable behaviour in molecular optimization tasks involving multiple, potentially conflicting constraints. By explicitly exposing intermediate reasoning steps, Logos enables human inspection and assessment of the design logic underlying each generated structure. These results indicate that jointly optimizing for reasoning structure and physical consistency offers a practical pathway toward reliable and interpretable AI systems for molecular science, supporting closer integration of artificial intelligence into scientific discovery processes.
