Imagine an AI that could understand the intricate language of molecules, translating their complex structures into human-readable text and vice versa. This isn't science fiction; it's the reality researchers are building with MolReFlect, a groundbreaking AI model that's changing how we interact with the molecular world. Drug discovery, materials science, and our understanding of the very building blocks of life depend on deciphering the complex code of molecules. Traditionally, translating between molecular structures (represented as SMILES strings or graphs) and descriptive text has been a laborious, manual process, often requiring expert chemists. Existing AI models, while powerful, often treat molecules as single units, overlooking the subtle relationships between molecular substructures and their corresponding textual descriptions. This lack of granularity limits the accuracy and explainability of AI-driven molecule-text translation. MolReFlect addresses this challenge with an innovative teacher-student approach. A larger “teacher” AI model, like Llama-2 70B, is first trained to extract key phrases from either the molecular structure or the text description. These extracted phrases, representing fine-grained alignments between molecular components and textual elements, become the building blocks for a more nuanced understanding. To refine this understanding, the teacher AI engages in “in-context selective reflection.” It examines previous extraction results, learning from past successes and refining its ability to discern the most relevant alignments. Then a smaller “student” AI, like Mistral 7B, selects from the most promising of these alignments to further hone the translation process. Finally, the student AI undergoes a specialized training process called “Chain-of-Thought In-Context Molecule Tuning” (CoT-ICMT). This process teaches the AI to reason through the translation, connecting the fine-grained alignments with the overall meaning of the molecular structure or textual description. By integrating the fine-grained alignments into training, this process also contributes to a more transparent and explainable framework for molecule-text translation. Tested on the ChEBI-20 dataset, a benchmark for molecule-caption translation, MolReFlect significantly outperforms existing methods. It achieves state-of-the-art results in both translating molecules to text (Mol2Cap) and generating molecules from text (Cap2Mol). Importantly, MolReFlect achieves this without needing additional data modalities like images or 3D structures, demonstrating the power of its fine-grained alignment strategy. The implications of this research are vast. By enabling more accurate and explainable molecule-text translation, MolReFlect can accelerate drug discovery by helping researchers quickly identify promising drug candidates. It can also revolutionize materials science by facilitating the design of new materials with specific properties. The ability to bridge the gap between molecular structures and human language could unlock new possibilities in synthetic biology and our understanding of biological processes. While promising, MolReFlect is still in its early stages. Future research could focus on improving the reflection process, exploring different teacher-student model combinations, and expanding the range of molecular data MolReFlect can handle. The journey toward a truly fluent molecular translator has just begun, but MolReFlect represents a significant leap forward.