SOTAVerified

Molecule Captioning

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.

Papers

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Showing 1–10 of 25 papers

TitleStatusHype
XMolCap: Advancing Molecular Captioning through Multimodal Fusion and Explainable Graph Neural NetworksCode0
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural LanguageCode0
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization—0
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
MolReFlect: Towards Fine-grained In-Context Alignment between Molecules and Texts—0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Vector-ICL: In-context Learning with Continuous Vector RepresentationsCode1
Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal FusionCode0
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
ReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text PretrainingCode1
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All datasetsChEBI-20L+M-24

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mol2Lang-VLMBLEU-277.7—Unverified
2XMolCapBLEU-277.4—Unverified
3MolT5-LargeBLEU-276.9—Unverified
4Nach0BLEU-273.81—Unverified
5MolT5-BaseBLEU-273.8—Unverified
6MolT5-SmallBLEU-270.9—Unverified