Molecule Captioning
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Benchmarks
Most implemented
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
MolFM: A Multimodal Molecular Foundation Model
XMolCap: Advancing Molecular Captioning through Multimodal Fusion and Explainable Graph Neural Networks
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
Papers
BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning
Bridging molecular structures and natural language is essential for controllable design. Autoregressive models struggle with long-range dependencies, while standard diffusion processes apply uniform corruption across pos…
Molecule CaptioningLanguage ModellingFactual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising
Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issues, we propose a non-autoregressive diffusion framework that generat…
Molecule CaptioningImproving Large Molecular Language Model via Relation-aware Multimodal Collaboration
Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the …
Molecule CaptioningCROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs
Recent advances in molecular science have been propelled significantly by large language models (LLMs). However, their effectiveness is limited when relying solely on molecular sequences, which fail to capture the comple…
Molecule CaptioningXMolCap: Advancing Molecular Captioning through Multimodal Fusion and Explainable Graph Neural Networks
Large language models (LLMs) have significantly advanced computational biology by enabling the integration of molecular, protein, and natural language data to accelerate drug discovery. However, existing molecular captio…
Drug DiscoveryMolecule CaptioningAutomatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. Th…
Drug DiscoveryMolecule CaptioningSentenceText-based de novo Molecule Generation