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MolReFlect: A New Framework for Fine-Grained Alignment Between Molecules and Texts

Large language models (LLMs) are transforming various scientific fields, including drug discovery and materials science. A key challenge in applying LLMs to molecules is effectively connecting molecular structures with their textual descriptions (captions). Existing methods often treat molecules as simple strings (like SMILES notation) or graphs, neglecting the crucial relationships between specific molecular substructures and the words that describe them. A new research paper introduces MolReFlect, a novel framework designed to address this limitation, achieving state-of-the-art results in molecule-caption translation.

The core innovation of MolReFlect lies in its ability to create fine-grained alignments between molecular substructures and their corresponding textual phrases. Imagine a molecule depicted as a complex diagram with various functional groups (e.g., a carboxyl group, an amino group, a benzene ring). MolReFlect aims to precisely link each of these groups to the specific words in the caption that describe them. For example, if the caption mentions “carboxylic acid,” MolReFlect would connect that phrase to the carboxyl group in the molecule’s structure.

MolReFlect accomplishes this through a teacher-student framework. A powerful teacher LLM initially identifies these alignments using the molecule’s SMILES string or the caption itself. This initial stage is termed Zero-shot Alignment Extraction. The process is “zero-shot” because it doesn’t require any pre-training on fine-grained alignment data; the LLM directly infers the alignments from the input.

However, these initial alignments might not be perfect. To refine them, MolReFlect incorporates In-Context Selective Reflection. The teacher LLM re-examines its initial alignments in the context of similar molecule-caption pairs. This allows the teacher to learn from its past mistakes and improve the alignment accuracy. A smaller student LLM then selects the most accurate alignments from the initial set and the refined set based on perplexity—a measure of how well the model understands the alignment.

Finally, Chain-of-Thought In-Context Molecule Tuning (CoT-ICMT) enhances the student LLM’s understanding. This step reframes the learning process using a chain-of-thought format, explicitly guiding the student to reason through the alignment and subsequently generate a caption or molecular structure based on the information extracted. The teacher LLM’s curated alignments are presented as context examples, guiding the student’s learning process.

The researchers evaluated MolReFlect on the ChEBI-20 dataset, a benchmark for molecule-caption translation. Results demonstrated that MolReFlect significantly outperforms existing methods, achieving state-of-the-art performance in both caption generation from molecular structures (Mol2Cap) and structure generation from captions (Cap2Mol). Ablation studies confirmed the importance of each stage in the MolReFlect framework, highlighting the value of both fine-grained alignments and the teacher-student learning approach.

Beyond its superior performance, MolReFlect also offers increased explainability. By explicitly linking molecular substructures to textual descriptions, the model’s predictions become easier to understand and interpret, which is particularly valuable in scientific contexts where transparency and accountability are paramount. The framework’s ability to work with general-purpose LLMs, without extensive domain-specific pre-training, adds to its appeal and potential broad applicability across various chemical and biological tasks. This research demonstrates a significant step forward in leveraging LLMs for molecule understanding and generation.