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george2cool36/
dna-structure-multitarget-mlp

Name: dna-structure-multitarget-mlp
Availability: InStock

pytorch

ImprovedNet

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Multi-output DNA Structure Regressor (PyTorch)

Description

This model is a multi-output DNA structure regressor built and trained from scratch in PyTorch.
It predicts six structural stability metrics — including Minimum Free Energy (MFE), number of base pairs, mean stem length, number of stems, number of hairpins, and number of internal loops — directly from engineered DNA sequence features.
Trained on the [aedupuga/2025-scaffold-structures] dataset, the model provides a fast, lightweight alternative to more complex and time-consuming simulation tools like NUPACK, enabling near-instant predictions for plasmid stability analysis.

Model

Architecture: 3-layer MLP (512→256→128, dropout 0.3)
Inputs: 109658 features
Outputs: 6 targets → mfe_energy, num_pairs, stem_len_mean, num_stems, num_hairpins, num_internal_loops
Loss: MSE
Optimizer: Adam (lr=0.0001)
Epochs: 15

Metrics (test)

Overall MSE: 15022.6787
Overall R²: -34.0313
Training time (s): 131.85
Prediction time (s): 0.2694

MAE per target

json

{
  "mfe_energy": 139.4054718017578,
  "num_pairs": 116.53337097167969,
  "stem_len_mean": 2.4054114818573,
  "num_stems": 69.17422485351562,
  "num_hairpins": 14.115099906921387,
  "num_internal_loops": 94.97564697265625
}

Usage

bash

pip install torch numpy
python inference.py

Ensure to apply any preprocessing (e.g., scaling, SVD) used during training.

Limitations

Performance is less reliable for shorter DNA strands, as the training data primarily consists of longer plasmid sequences.
The model is intended for educational and exploratory research use, not for experimental or clinical validation.

george2cool36/dna-structure-multitarget-mlp