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Protein Matryoshka Embeddings

Developed by monsoon-nlp
This model generates embedding vectors for protein sequences, supporting shortened embeddings to accelerate search tasks.
Downloads 2,121
Release Time : 3/24/2024

Model Overview

A protein sequence embedding model based on Rostlab/prot_bert_bfd, trained with matryoshka loss function, suitable for protein similarity calculation in the field of biology.

Model Features

Matryoshka Embedding Technology
Supports generating embedding vectors of different lengths, allowing a balance between accuracy and efficiency based on task requirements.
Specialized Protein Processing
Optimized for IUPAC-IUB encoded protein sequences, directly processing amino acid sequences.
High-Performance Similarity Calculation
Achieves a cosine similarity metric of 0.92+ on the UniProt dataset.

Model Capabilities

Protein sequence embedding generation
Protein similarity calculation
Biological sequence feature extraction

Use Cases

Bioinformatics
Protein Function Prediction
Infers the function of unknown proteins through embedding vector similarity.
Protein Structure Classification
Classifies protein secondary/tertiary structures based on sequence embeddings.
Performs well on the TAPE benchmark.
Drug Development
Target Protein Screening
Rapidly screens candidate proteins with structures similar to the target protein.
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