Implementation of Progen in Pytorch, from the paper "ProGen: Language Modeling for Protein Generation"
GPT for proteins sequences
- Lucidrains
- Agorians
pip install progen-torch
import torch
from progen.model import ProGen
x = torch.randint(0, 100, (1, 1024))
# Initialize the model with specific parameters
model = ProGen(
num_tokens=100, # The size of the vocabulary
dim=512, # The dimension of the embeddings
seq_len=1024, # The length of the sequences
depth=6, # The number of layers in the model
window_size=256, # The size of the window for local attention
global_mlp_depth=2, # The depth of the MLP in the global attention mechanism
heads=8, # The number of attention heads
dim_head=512, # The dimension of each attention head
ff_mult=4, # The multiplier for the feed-forward network's hidden layer size
ff_glu=True, # Whether to use a GLU activation in the feed-forward network
attn_dim=None, # The dimension of the attention mechanism (None means it defaults to `dim`)
clamp_gate=True, # Whether to clamp the gate values in the GLU activation
shift_tokens=True, # Whether to shift the tokens for the causal attention mechanism
dropout=0.1, # The dropout rate
)
# Forward pass through the model
logits = model(x)
# The output is the logits for each token in the vocabulary, for each position in the input sequences
# Shape: (batch_size, sequence_length, num_tokens)
print(logits.shape) # Should print: torch.Size([1, 1024, 100])
Here is a table of the datasets used in the paper with metadata and source links:
| Dataset | Description | Source |
|---|---|---|
| Uniparc | Contains protein sequences from various sources | https://www.uniprot.org/uniparc/ |
| UniprotKB | Contains protein sequences and annotations | https://www.uniprot.org/uniprot/ |
| SWISS-PROT | Curated protein sequence database | https://www.uniprot.org/swiss-prot/ |
| TrEMBL | Computer-annotated protein sequences | https://www.uniprot.org/trembl/ |
| Pfam | Database of protein families | https://pfam.xfam.org/ |
| NCBI taxonomy | Taxonomic classification of organisms | https://www.ncbi.nlm.nih.gov/taxonomy |
Here is a diagram showing the data preprocessing flow:
graph TD
A[Uniparc] --> B[Filter and merge]
C[UniprotKB] --> B
D[SWISS-PROT] --> B
E[TrEMBL] --> B
F[Pfam] --> B
G[NCBI taxonomy] --> B
B --> H[Train/test split]
H --> I[Train set]
H --> J[ID test set]
H --> K[OOD test set]
The Uniparc, UniprotKB, SWISS-PROT, TrEMBL, Pfam, and NCBI taxonomy datasets are filtered and merged in step B. The aggregated dataset is then split into training, in-distribution test, and out-of-distribution test sets in step H.
MIT