| --- |
| language: en |
| license: mit |
| tags: |
| - toxicity |
| - cheminformatics |
| - nuclear-receptors |
| - sklearn |
| - svm |
| - rdkit |
| - drug-discovery |
| library_name: sklearn |
| --- |
| |
| # NR-ToxPred Models |
|
|
| Pre-trained machine learning models for predicting the binding activity of small molecules against **nine human nuclear receptors (NRs)**. |
|
|
| These models are used by the [NR-ToxPred GUI application](https://github.com/gokulalgates/NRToxPred-GUI) — a desktop app that requires no coding experience. |
|
|
| --- |
|
|
| ## What this repository contains |
|
|
| | Folder | Contents | |
| |--------|----------| |
| | `MODELS/morgan/` | SVM classifiers trained on Morgan (ECFP6) fingerprints — one per receptor | |
| | `MODELS/MACCS/` | SVM classifiers trained on MACCS Keys — one per receptor | |
| | `MODELS/ARclasses.npy` | Label encoder (Active / Inactive) | |
| | `X_train/` | Training set SMILES used for Applicability Domain assessment | |
|
|
| > SuperLearner ensemble models are not included here due to their size (1–1.5 GB each). |
|
|
| --- |
|
|
| ## Receptors covered |
|
|
| | Receptor | Full Name | |
| |----------|-----------| |
| | AR | Androgen Receptor | |
| | ERA | Estrogen Receptor Alpha | |
| | ERB | Estrogen Receptor Beta | |
| | FXR | Farnesoid X Receptor | |
| | GR | Glucocorticoid Receptor | |
| | PPARD | Peroxisome Proliferator-Activated Receptor Delta | |
| | PPARG | Peroxisome Proliferator-Activated Receptor Gamma | |
| | PR | Progesterone Receptor | |
| | RXR | Retinoid X Receptor | |
|
|
| --- |
|
|
| ## How to use |
|
|
| ### Option A — Desktop GUI (recommended, no coding needed) |
|
|
| Download the NR-ToxPred GUI from GitHub and run the installer. The app will download these models automatically on first launch. |
|
|
| 👉 **[NR-ToxPred GUI on GitHub](https://github.com/gokulalgates/NRToxPred-GUI)** |
|
|
| ### Option B — Python (programmatic use) |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import pickle, numpy as np |
| from rdkit import Chem |
| from rdkit.Chem import AllChem |
| |
| # Download a model |
| model_path = hf_hub_download( |
| repo_id="gokulalgates/nrtoxpred-models", |
| filename="MODELS/morgan/ARsvm_best.model", |
| repo_type="model", |
| ) |
| |
| # Load model |
| model = pickle.load(open(model_path, "rb")) |
| |
| # Generate Morgan fingerprint (ECFP6, 1024 bits) |
| mol = Chem.MolFromSmiles("CC(C)(c1ccc(O)cc1)c1ccc(O)cc1") # bisphenol A |
| fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=3, nBits=1024) |
| X = np.array(fp).reshape(1, -1) |
| |
| # Predict |
| label_enc = {0: "Inactive", 1: "Active"} |
| pred = model.predict(X)[0] |
| print(f"AR prediction: {pred}") |
| ``` |
|
|
| --- |
|
|
| ## Model details |
|
|
| | Property | Value | |
| |----------|-------| |
| | Algorithm | Support Vector Machine (SVM) | |
| | Fingerprints | Morgan ECFP6 (radius=3, 1024 bits) and MACCS Keys (167 bits) | |
| | Framework | scikit-learn 0.23.2 | |
| | Task | Binary classification (Active / Inactive) | |
| | Applicability Domain | Tanimoto fingerprint similarity to training set | |
|
|
| --- |
|
|
| ## Applicability Domain |
|
|
| Each prediction comes with a reliability label: |
|
|
| - **Reliable** — the compound is similar (Tanimoto ≥ 0.25) to at least one training set compound |
| - **Unreliable** — the compound lies outside the training chemical space; interpret with caution |
|
|
| The `X_train/` folder contains the training set SMILES used to compute these assessments. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use these models in your research, please cite: |
|
|
| > Predicting the binding of small molecules to nuclear receptors using machine learning. |
| > *Brief Bioinform.* 2022 May 13;23(3):bbac114. |
| > doi: [10.1093/bib/bbac114](https://doi.org/10.1093/bib/bbac114) |
|
|
| --- |
|
|
| ## License |
|
|
| MIT License |
|
|
