LightChem: A Lightweight Domain-Specific Language Model for Molecular Property and Reaction Prediction in Chemistry

Large language models (LLMs) have achieved breakthroughs in natural language processing, yet their use in chemistry is limited by insufficient domain knowledge, high computational demands, and poor scalability. To overcome these challenges, we developed LightChem, a lightweight model that integrates retrieval-augmented generation (RAG) and tool-calling capabilities, providing a platform driven by chemical knowledge and theoretical calculations. LightChem adopts a three-layer architecture, data aggregation, intelligent retrieval, and specialized tool integration, supported by the self-developed RAPTOR system for real-time literature updates. The platform incorporates a suite of computational modules, including automated molecular property prediction (e.g., PoLogP), retrosynthetic planning via ReSynZ, and scalable quantum chemistry packages such as CIM and GEBF, enabling high-accuracy simulations of complex systems. Case studies demonstrate successful predictions of lipophilicity, excitation/emission spectra, and synthetic pathways for drug-like molecules, while large-scale calculations reproduce binding energies in zeolite clusters and excitation energies in GFP chromophores with accuracy comparable to experimental values. Beyond core computations, LightChem features a user-friendly web interface that connects sophisticated algorithms with practical laboratory workflows. It provides modules for reagent management, synthesis assessment, safety evaluation, and experimental design, thereby lowering the barrier for non-specialists and supporting systematic research in photosensitive and functional molecules. Applications to gold nanoclusters and zeolites further highlight the platform’s potential in photocatalysis and optical materials, while also revealing current limitations in predicting HOMO–LUMO gaps and optical properties of complex aggregates. LightChem demonstrates good performance in molecular property prediction, retrosynthetic planning, and large-scale quantum simulations, making it a versatile assistant for chemical research, bridging knowledge-driven reasoning with first-principles accuracy.