MuthuS97
/

structuralmodule-protease_inhibitors

@@ -12,264 +12,78 @@ tags:
 - biology
 - bioinformatics
 - unsupervised-learning
 datasets:
 - MEROPS
 - UniProt
 - AlphaFold
 model_name: Structural_module-protease_inhibitor
-model_description: |
-  Structural_module-protease_inhibitoris  an unsupervised, one-class deep learning model for filtering protein
-  structures that are structurally inconsistent with curated protease inhibitor (PI) like features learned from (RCSBembeddingmodel, github.com/rcsb/rcsb-embedding-model) from protease inhibitor databases. The model learns the structural embedding manifold of known protease
-  inhibitors  and assigns higher reconstruction error to structurally dissimilar inputs.
-user_interface: |
-  Easy-to-use inference interface:
-  "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JLhLpvXG4plzPtIliG_CJnYui6P8Pu1J?usp=sharing)"
-training_data_description: |
-  The model was trained on 17,889 curated protease inhibitor structures from the MEROPS
-  database. MEROPS sequences were mapped via similarity search against taxonomy-
-  restricted UniProt datasets (fungi, plants, bacteria), and corresponding structures
-  were obtained from the AlphaFold Protein Structure Database and used for traning the model.
-input_format: |
-  Fixed-length continuous protein structure embeddings derived from three-dimensional
-  structural features (RCSBembedding model, github.com/rcsb/rcsb-embedding-model). Embeddings must be standardized using the provided scaler.pkl
-  before inference.
-model_architecture: |
-  Fully connected autoencoder implemented in PyTorch via the PyOD library, featuring
-  a geometrically decreasing encoder, latent bottleneck, symmetric decoder, batch
-  normalization, dropout regularization, and mean squared reconstruction loss.
-training_procedure: |
-  The model was trained using the Adam optimizer with weight decay and mini-batch
-  stochastic gradient descent. Hyperparameters were optimized using Bayesian
-  optimization (Optuna, TPE sampler) on an independent 10% validation split
-  (~1,789 structures). The tuning objective was to minimize reconstruction error on
-  unseen but structurally valid protease inhibitor examples. The final model was
-  retrained on the full dataset using the optimal hyperparameters with fixed random
-  seeds.
-outputs: |
-  The model outputs a reconstruction-based anomaly score, an outlier probability,
-  and a confidence estimate. Low reconstruction-based anomaly scores indicate structural consistency with known
-  protease inhibitor folds, while high scores indicate structural dissimilarity.
-intended_use: |
-  Structural filtering and pre-selection of protease inhibitor–like protein structures
-  in large-scale datasets.
-limitations: |
-  Novel PI folds absent from the training data may be incorrectly rejected.
-not_intended_use: |
-  Functional annotation, clinical
-  decision-making.
-reproducibility: |
-  All preprocessing parameters, training configurations are provided
-  to enable exact reproduction of results.
-citation: |
-  Please cite the associated publication and acknowledge the PyOD library, the MEROPS
-  protease inhibitor database, and the AlphaFold Protein Structure Database.
 ---
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 - biology
 - bioinformatics
 - unsupervised-learning
 datasets:
 - MEROPS
 - UniProt
 - AlphaFold
 model_name: Structural_module-protease_inhibitor
+metrics:
+- reconstruction_error
 ---
+# Structural_module-protease_inhibitor
+This model is an unsupervised, one-class deep learning autoencoder designed to filter protein structures. It identifies candidates that are structurally inconsistent with curated protease inhibitor (PI) features learned from known PI databases.
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JLhLpvXG4plzPtIliG_CJnYui6P8Pu1J?usp=sharing)
+## Model Description
+The model learns the structural embedding manifold of known protease inhibitors and assigns a **reconstruction error** to inputs. High reconstruction errors indicate structural dissimilarity from the training distribution, allowing for the filtering of non-PI-like structures.
+* **Model type:** Fully connected Autoencoder (via PyOD)
+* **License:** [Specify License, e.g., MIT, Apache 2.0]
+* **embeddings calculated using:** [RCSB embedding model](https://github.com/rcsb/rcsb-embedding-model)
+## Intended Uses & Limitations
+### Intended Use
+* Structural filtering and pre-selection of protease inhibitor–like protein structures in large-scale datasets.
+* Quality control for generated or predicted protein structures in the context of PIs.
+### Limitations
+* **Novel Folds:** Novel PI folds absent from the MEROPS training data may be incorrectly rejected (false positives for anomalies).
+* **Not for Clinical Use:** This model is not intended for functional annotation or clinical decision-making.
+## Training Data
+The model was trained on **17,889 curated protease inhibitor structures**:
+1.  **Source:** MEROPS database.
+2.  **Mapping:** Sequences were mapped via similarity search against taxonomy-restricted UniProt datasets (fungi, plants, bacteria).
+3.  **Structures:** Corresponding 3D structures were obtained from the **AlphaFold Protein Structure Database using uniprot Ids**.
+## Technical Specifications
+### Input Format
+The model accepts **fixed-length continuous protein structure embeddings** derived from the RCSB embedding model.
+> **Important:** Embeddings must be standardized using the provided `scaler.pkl` before inference.
+### Architecture
+Implemented in PyTorch via the PyOD library:
+* **Encoder:** Geometrically decreasing layers.
+* **Bottleneck:** Latent representation layer.
+* **Decoder:** Symmetric reconstruction layers.
+* **Regularization:** Batch normalization and dropout.
 ### Training Procedure
+* **Optimizer:** Adam with weight decay.
+* **Hyperparameter Tuning:** Optimized via Bayesian optimization (Optuna, TPE sampler) on a 10% validation split (~1,789 structures).
+* **Objective:** Minimize Mean Squared Error (MSE) reconstruction loss on validation set.
+## How to Get Started
+To use this model, ensure you have `pyod` and `torch` installed.
+```python
+import job_lib
+import torch
+from pyod.models.auto_encoder_torch import AutoEncoder
+# 1. Load the scaler and standardize your RCSB embeddings
+scaler = joblib.load('scaler.pkl')
+scaled_embeddings = scaler.transform(raw_embeddings)
+# 2. Load the model (ensure PyOD environment is set up)
+# model = joblib.load('model.pkl')
+# 3. Predict
+anomaly_scores = model.decision_function(scaled_embeddings)
+labels = model.predict(scaled_embeddings) # 0 for inliers (PI-like), 1 for outliers