MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
cs.AI, cs.LG
Submitted: 2026-08-24
Updated: 2026-08-24
Terminology
Sources
- GPT-4 Technical Report
- ChemBERTa-2: Towards Chemical Foundation Models
- Intern-S1: A Scientific Multimodal Foundation Model
- Qwen3-VL Technical Report
- LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
- LLM2Vec-Gen: Generative Embeddings from Large Language Models
- ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
- Strategies for Pre-training Graph Neural Networks
- VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
- Gemini Embedding: Generalizable Embeddings from Gemini
- RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature
- Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
- Pre-training Molecular Graph Representation with 3D Geometry
- MolFM: A Multimodal Molecular Foundation Model
- A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
- InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
- ChemMLLM: Chemical Multimodal Large Language Model
- DeepSeek-OCR 2: Visual Causal Flow
- Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
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