biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-SIDER-101
| Property | Value |
|---|---|
| Parameter Count | 84.6M |
| License | Apache 2.0 |
| Architecture | Multi-view Molecular Embedding with Late Fusion (MMELON) |
| Paper | Multi-view biomedical foundation models |
What is biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-SIDER-101?
This is a sophisticated multimodal biomedical foundation model developed by IBM Research for analyzing small molecules. It employs the MMELON (Multi-view Molecular Embedding with Late Fusion) approach to combine different molecular representations - images, graphs, and text - into a unified framework for molecular property prediction.
Implementation Details
The model implements three distinct views of molecular structures: 2D visual depictions generated using RDKit, graph representations encoding atom and bond properties, and SMILES string text representations. These are processed through specialized encoders and combined using an attention-based aggregator.
- Image View: Captures 2D molecular structure with data augmentation
- Graph View: Represents molecules as undirected graphs with atom and bond properties
- Text View: Processes SMILES strings using a transformer architecture
Core Capabilities
- Molecular property prediction (regression and classification tasks)
- Chemical library similarity searching
- Integration with protein embeddings for combined analysis
- Binding affinity, solubility, and toxicity predictions
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its multi-view approach, combining three different molecular representations to achieve robust performance across various property prediction tasks. Unlike single-view models that excel in specific areas, this model maintains high performance across diverse applications.
Q: What are the recommended use cases?
The model is specifically designed for drug-like molecules under 1000 Da molecular weight. It's ideal for drug discovery applications, including lead finding, optimization, and molecular property prediction. However, it's not intended for molecular generation tasks.
