MolReFlect: Towards In-Context Fine-grained Alignments between Molecules
  and Texts
Molecule discovery is a pivotal research field, impacting everything from the
medicines we take to the materials we use. Recently, Large Language Models
(LLMs) have been widely adopted in molecule understanding and generation, yet
the alignments between molecules and their corresponding captions remain a
significant challenge. Previous endeavours often treat the molecule as a
general SMILES string or molecular graph, neglecting the fine-grained
alignments between the molecular sub-structures and the descriptive textual
phrases, which are crucial for accurate and explainable predictions. In this
case, we introduce MolReFlect, a novel teacher-student framework designed to
contextually perform the molecule-caption alignments in a fine-grained way. Our
approach initially leverages a larger teacher LLM to label the detailed
alignments by directly extracting critical phrases from molecule captions or
SMILES strings and implying them to corresponding sub-structures or
characteristics. To refine these alignments, we propose In-Context Selective
Reflection, which retrieves previous extraction results as context examples for
teacher LLM to reflect and lets a smaller student LLM select from in-context
reflection and previous extraction results. Finally, we enhance the learning
process of the student LLM through Chain-of-Thought In-Context Molecule Tuning,
integrating the fine-grained alignments and the reasoning processes within the
Chain-of-Thought format. Our experimental results demonstrate that MolReFlect
enables LLMs like Mistral-7B to significantly outperform the previous
baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement
not only enhances the generative capabilities of LLMs in the molecule-caption
translation task, but also contributes to a more explainable framework.
6746d91b1c6c176064e0d558
Jiatong Li
claimed_verified
2024-11-27T22:19:51.992Z
6438e55cb2ea24b52ebc45ec
/avatars/e13c7398f77e7e0bd5eed03102aa5c36.svg
Jiatong LI
user
phenixace
6746d91b1c6c176064e0d559
Yunqing Liu
6746d91b1c6c176064e0d55a
Wei Liu
6746d91b1c6c176064e0d55b
Jingdi Le
6746d91b1c6c176064e0d55c
Di Zhang
extracted_confirmed
2024-11-28T07:45:08.818Z
64bce15bafd1e46c5504ad38
https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/vkEjiu-mIagKlrXzDH75o.png
Di Zhang
user
di-zhang-fdu
6746d91b1c6c176064e0d55d
Wenqi Fan
6746d91b1c6c176064e0d55e
Dongzhan Zhou
6746d91b1c6c176064e0d55f
Yuqiang Li
6746d91b1c6c176064e0d560
Qing Li
MolReFlect: Towards In-Context Fine-grained Alignments between Molecules
  and Texts
Molecule discovery is a pivotal research field, impacting everything from the
medicines we take to the materials we use. Recently, Large Language Models
(LLMs) have been widely adopted in molecule understanding and generation, yet
the alignments between molecules and their corresponding captions remain a
significant challenge. Previous endeavours often treat the molecule as a
general SMILES string or molecular graph, neglecting the fine-grained
alignments between the molecular sub-structures and the descriptive textual
phrases, which are crucial for accurate and explainable predictions. In this
case, we introduce MolReFlect, a novel teacher-student framework designed to
contextually perform the molecule-caption alignments in a fine-grained way. Our
approach initially leverages a larger teacher LLM to label the detailed
alignments by directly extracting critical phrases from molecule captions or
SMILES strings and implying them to corresponding sub-structures or
characteristics. To refine these alignments, we propose In-Context Selective
Reflection, which retrieves previous extraction results as context examples for
teacher LLM to reflect and lets a smaller student LLM select from in-context
reflection and previous extraction results. Finally, we enhance the learning
process of the student LLM through Chain-of-Thought In-Context Molecule Tuning,
integrating the fine-grained alignments and the reasoning processes within the
Chain-of-Thought format. Our experimental results demonstrate that MolReFlect
enables LLMs like Mistral-7B to significantly outperform the previous
baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement
not only enhances the generative capabilities of LLMs in the molecule-caption
translation task, but also contributes to a more explainable framework.
MolReFlect, a teacher-student framework, enhances molecule-caption alignment through fine-grained extraction, reflection, and tuning, achieving state-of-the-art performance on the ChEBI-20 dataset.
Large Language Models MolReFlect molecular sub-structures SMILES strings molecular graph captions teacher-student framework In-Context Selective Reflection Chain-of-Thought In-Context Molecule Tuning ChEBI-20 dataset