Molecule Captioning
19 papers with code • 2 benchmarks • 2 datasets
Molecular description generation entails the creation of a detailed textual depiction illuminating the structure, properties, biological activity, and applications of a molecule based on its molecular descriptors. It furnishes chemists and biologists with a swift conduit to essential molecular information, thus efficiently guiding their research and experiments.
Benchmarks
These leaderboards are used to track progress in Molecule Captioning
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ChEBI-20
L+M-24
Most implemented papers
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
bingsu12/momu
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Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
MolFM: A Multimodal Molecular Foundation Model
biofm/openbiomed
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In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs.
Translation between Molecules and Natural Language
We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.
Graph-based Molecular Representation Learning
zaixizhang/MGSSL
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Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
Unifying Molecular and Textual Representations via Multi-task Language Modelling
gt4sd/multitask_text_and_chemistry_t5
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Here, we propose the first multi-domain, multi-task language model that can solve a wide range of tasks in both the chemical and natural language domains.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
ai-hpc-research-team/git-mol
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Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules.
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
Furthermore, our method shows a sustained improvement as the volume of pseudo data increases, revealing the great potential of pseudo data in advancing low-resource cross-modal molecule discovery.
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
QizhiPei/BioT5
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Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
acharkq/molca
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MolCA enables an LM (e. g., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector.
