Research map / Graph learning / Graph self-supervised learning
Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- MORE: Molecule Pretraining with Multi-Level Pretext Task (AAAI 2025 · similarity 0.88)
- Bridging Molecular Graphs and Large Language Models (AAAI 2025 · similarity 0.88)
- Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck (AAAI 2025 · similarity 0.87)
- Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation (ICLR 2025 · similarity 0.87)
- UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning (ICML 2024 · similarity 0.86)
- LLaMo: Large Language Model-based Molecular Graph Assistant (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
