# Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment

arXiv: https://arxiv.org/abs/2510.26157 · HTML: https://mlsanity.com/paper/2510.26157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing](https://mlsanity.com/paper/2212.10789/) (2022)
- [A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language](https://mlsanity.com/paper/2209.05481/) (2022)
- [MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter](https://mlsanity.com/paper/2310.12798/) (2023)
- [Unifying Molecular and Textual Representations via Multi-task Language Modelling](https://mlsanity.com/paper/2301.12586/) (2023)
- [Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective](https://mlsanity.com/paper/2306.06615/) (2023)
- [InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery](https://mlsanity.com/paper/2311.16208/) (2023)
- [GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text](https://mlsanity.com/paper/2308.06911/) (2023)
- [MolXPT: Wrapping Molecules with Text for Generative Pre-training](https://mlsanity.com/paper/2305.10688/) (2023)
- [BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning](https://mlsanity.com/paper/2402.17810/) (2024)
- [MolFM: A Multimodal Molecular Foundation Model](https://mlsanity.com/paper/2307.09484/) (2023)
- [Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders](https://mlsanity.com/paper/2008.05231/) (2020)
- [Multimodal Molecular Pretraining via Modality Blending](https://mlsanity.com/paper/2307.06235/) (2023)
- [Large Language Models are In-Context Molecule Learners](https://mlsanity.com/paper/2403.04197/) (2024)
- [MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts](https://mlsanity.com/paper/2411.14721/) (2024)
- [Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval](https://mlsanity.com/paper/2411.11875/) (2024)
- [GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules](https://mlsanity.com/paper/2411.10821/) (2024)

## Built on by (most cited first, 2 of 2)

- [Retrieval-guided Twin Fusion with Similarity-aware Contrast for Molecule-Text Alignment](https://mlsanity.com/paper/2608.16005/) (2026)
- [MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs](https://mlsanity.com/paper/2609.23073/) (2026)

## Abstract (arXiv)

Molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information. However, existing models often struggle to capture subtle differences between molecules and their descriptions, as they lack the ability to learn fine-grained alignments between molecular substructures and chemical phrases. To address this limitation, we introduce MolBridge, a novel molecule-text learning framework based on substructure-aware alignments. Specifically, we augment the original molecule-description pairs with additional alignment signals derived from molecular substructures and chemical phrases. To effectively learn from these enriched alignments, MolBridge employs substructure-aware contrastive learning, coupled with a self-refinement mechanism that filters out noisy alignment signals. Experimental results show that MolBridge effectively captures fine-grained correspondences and outperforms state-of-the-art baselines on a wide range of molecular benchmarks, highlighting the significance of substructure-aware alignment in molecule-text learning.