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tuankg1028/
MTL-Peptide-Classifier

Name: MTL-Peptide-Classifier
Availability: InStock

facebook/esm2_t33_650M_UR50D

finetune

mit

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MTL Peptide Classifier (22 Tasks)

Multi-Task Learning peptide classifier covering 22 binary peptide-activity tasks. Built on a frozen ESM-2 (650M) backbone with a parallel Transformer + CNN feature extractor and per-task heads, following a PDeepPP-inspired design.

Held-out Test Set Performance (Averaged across 22 tasks)

Metric	Value
Accuracy	86.63%
F1	84.99%
AUC	93.47%
MCC	72.67%

Best Val Avg F1 (used for checkpoint selection): 85.56%

Per-Task Test Metrics

Task	ACC	F1	AUC	MCC
AntiMRSA	0.9899	0.9667	0.9970	0.9607
Anticancer	0.7035	0.7344	0.8007	0.4184
ACE_inhibitory	0.6801	0.7392	0.8355	0.4040
Antioxidant	0.7117	0.7309	0.8173	0.4382
Bitter	0.8203	0.8456	0.9591	0.6782
Antimalarial	0.9736	0.7692	0.9177	0.7579
Anti_inflammatory	0.9886	0.9887	0.9979	0.9773
Antimicrobial	0.9746	0.9552	0.9915	0.9379
Signal_peptide	0.9927	0.9927	0.9997	0.9854
Antifungal	0.9465	0.9456	0.9863	0.8935
Antimalarial_alt	0.9877	0.9630	0.9942	0.9566
Anticancer_alt	0.9330	0.9316	0.9784	0.8667
Anti_parasitic	0.7826	0.7500	0.9216	0.5855
Umami	0.8427	0.7308	0.9297	0.6243
Quorum_sensing	0.9250	0.9231	0.9850	0.8511
Antibacterial	0.9431	0.9424	0.9789	0.8863
NeuroPred	0.8660	0.8543	0.9444	0.7416
Toxicity	0.9086	0.8971	0.9699	0.8178
Antiviral	0.8307	0.8319	0.9098	0.6614
DPPIV_inhibitory	0.8647	0.8732	0.9478	0.7361
BBP	0.6579	0.5185	0.9141	0.3873
TTCA	0.7360	0.8129	0.7858	0.4221

Architecture

Shared encoder: frozen ESM-2 (facebook/esm2_t33_650M_UR50D, 650M params) + learnable base embedding, mixed at esm_ratio=0.9
Feature extraction (parallel): 4-layer Transformer + CNN (kernel=7, padding=3) → concatenated to 2560-dim features
Heads: 22 binary classifiers (2560 → 256 → 128 → 2) with masked average pooling
Loss: TIM (Threshold-Independent Multi-task) loss + label smoothing 0.1

Tasks

#	Task	Source
1	ACE_inhibitory	UniDL4BioPep
2	DPPIV_inhibitory	UniDL4BioPep
3	Bitter	UniDL4BioPep
4	Umami	UniDL4BioPep
5	Antimicrobial	UniDL4BioPep
6	Antimalarial (main)	UniDL4BioPep
7	Antimalarial_alt	UniDL4BioPep
8	Quorum_sensing	UniDL4BioPep
9	Anticancer (main)	UniDL4BioPep
10	Anticancer_alt	UniDL4BioPep
11	AntiMRSA	UniDL4BioPep
12	TTCA	UniDL4BioPep
13	BBP	UniDL4BioPep
14	Anti_parasitic	UniDL4BioPep
15	NeuroPred	UniDL4BioPep
16	Antibacterial	UniDL4BioPep
17	Antifungal	UniDL4BioPep
18	Antiviral	UniDL4BioPep
19	Toxicity	UniDL4BioPep
20	Anti_inflammatory	local dataset
21	Signal_peptide	local dataset
22	Antioxidant	UniDL4BioPep (antioxidant_FRS)

Usage

python

import os
from huggingface_hub import hf_hub_download
import torch
from transformers import EsmTokenizer

from mtl_peptide_classifier import MTLPeptideClassifier, get_all_peptide_tasks

REPO = "tuankg1028/MTL-Peptide-Classifier"
checkpoint_dir = "MTL-Peptide-Classifier"
os.makedirs(checkpoint_dir, exist_ok=True)

for fname in ["heads.pt", "shared_backbone.pt", "ablation_config.json"]:
    hf_hub_download(repo_id=REPO, filename=fname, local_dir=checkpoint_dir)

tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
task_configs = get_all_peptide_tasks("datasets")  # needs local datasets/ dir for task names

model = MTLPeptideClassifier(
    task_configs=task_configs,
    hidden_dim=1280,
    esm_ratio=0.9,
    num_transformer_layers=4,
    dropout=0.3,
    use_transformer=True,
    use_cnn=True,
    unfreeze_esm=False,
)

device = "cuda" if torch.cuda.is_available() else "cpu"
backbone = torch.load(f"{checkpoint_dir}/shared_backbone.pt", map_location=device)
heads = torch.load(f"{checkpoint_dir}/heads.pt", map_location=device)

model.base_embed.load_state_dict(backbone["base_embed"])
if "transformer" in backbone:
    model.transformer.load_state_dict(backbone["transformer"])
if "cnn" in backbone:
    model.cnn.load_state_dict(backbone["cnn"])
    model.layer_norm.load_state_dict(backbone["layer_norm"])
for name, head in model.heads.items():
    if name in heads:
        head.load_state_dict(heads[name])

model = model.to(device).eval()

sequence = "MKWVTFISLLFLFSSAYSRGVFRR"
tokens = " ".join(list(sequence))
inputs = tokenizer(tokens, return_tensors="pt", max_length=128, padding="max_length", truncation=True)
with torch.no_grad():
    logits = model(inputs["input_ids"].to(device), inputs["attention_mask"].to(device), task_name="Antimicrobial")
    probs = torch.softmax(logits, dim=-1)

Training

Base model: facebook/esm2_t33_650M_UR50D (frozen)
Batch size: 16, learning rate: 1e-4, 50 epochs, dropout: 0.3
3-way split per task: 80% train / 20% val (checkpoint selection) / held-out test CSV evaluated once
Mixed precision, gradient clipping 1.0, cosine LR with 5 warmup epochs
TIM loss + label smoothing 0.1

Files

heads.pt — per-task classifier heads
shared_backbone.pt — base embedding, Transformer, CNN, LayerNorm
ablation_config.json — architecture configuration for reproducibility
test_results.json — held-out test metrics (per task + averages)
mtl_peptide_classifier.py — model code

Requirements

torch>=2.0.0
transformers>=4.30.0
huggingface_hub
numpy
pandas
scikit-learn