Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
cs.LG, cs.AI
Submitted: 2026-08-23
Updated: 2026-09-01
Code: https://github.com/Boehringer-Ingelheim/mol-jepa
Terminology
Sources
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- stable-pretraining-v1: Foundation Model Research Made Simple
- Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
- ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
- Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
- GraphCL: Contrastive Self-Supervised Learning of Graph Representations
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
- Understanding Dimensional Collapse in Contrastive Self-supervised Learning
- When to Align, When to Predict: A Phase Diagram for Multimodal Learning
- Semi-Supervised Classification with Graph Convolutional Networks
- WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
- MolFM: A Multimodal Molecular Foundation Model
- MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning
- Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
- TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
- Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
- Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning
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