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Materials.selfies Ted
M
Materials.selfies Ted
Developed by ibm-research
A Transformer-based encoder-decoder model specifically designed for molecular representation using SELFIES
Downloads 3,343
Release Time : 10/25/2024
Model Overview
This model utilizes the Transformer architecture to process molecular representations in SELFIES format, capable of converting SMILES strings to SELFIES format and generating molecular embedding vectors, suitable for molecular characterization tasks in chemistry and materials science.
Model Features
SELFIES Molecular Representation
Uses SELFIES (Self-referencing Embedded Strings) format for molecular representation, offering stronger robustness compared to traditional SMILES format
Transformer Architecture
Based on advanced Transformer architecture, effectively capturing complex patterns and relationships in molecular structures
Molecular Embedding Generation
Converts molecular structures into meaningful embedding vectors for downstream machine learning tasks
Model Capabilities
SMILES to SELFIES Conversion
Molecular Feature Extraction
Molecular Embedding Generation
Use Cases
Cheminformatics
Molecular Similarity Analysis
Evaluates molecular structural similarity by comparing molecular embedding vectors
Can be used for lead compound screening in drug discovery
Molecular Property Prediction
Uses molecular embeddings as input features for machine learning models
Improves accuracy of molecular property prediction models
Materials Science
New Material Discovery
Conducts material design and screening based on molecular characterization
Accelerates the development process of new functional materials
Safetensors
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