| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - drug-discovery |
| - molecular-property-prediction |
| - pxr |
| - admet |
| - xgboost |
| pretty_name: "PXR Activity Prediction - Method Report (VIDraft)" |
| task_categories: |
| - tabular-regression |
| --- |
| |
| # PXR Activity Prediction — Method Report |
|
|
| **Team:** VIDraft |
| **Website:** https://www.vidraft.net |
| **Contact:** arxivgpt@gmail.com |
| **Challenge:** OpenADMET PXR Blind Challenge — Activity Prediction Track |
| **Submitted:** 2026-06-11 |
|
|
| --- |
|
|
| ## Abstract |
|
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| We present an XGBoost-based ensemble approach for predicting PXR (Pregnane X Receptor) agonist activity (pEC50) using molecular fingerprints. Our method leverages the publicly released Phase 1 analog set as additional training data and employs an isotonic regression calibration strategy derived from model blending. On the Phase 1 holdout, our model achieves RAE = 0.444 and Spearman rho = 0.944. |
|
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| --- |
|
|
| ## 1. Data |
|
|
| ### 1.1 Training Data |
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| We used all provided data sources, including the **Phase 1 analog set (Analog Set 1)** as additional training examples, following the official challenge guidance that these labels are released for participants to incorporate into their training pipelines. |
|
|
| | Dataset | Molecules | Source | |
| |---------|-----------|--------| |
| | Train set | 4,139 | Official challenge training data | |
| | Counter screen | 2,647 | Official counter-assay data | |
| | Phase 1 (Analog Set 1) | 253 | Publicly released Phase 1 labels | |
| | **Total** | **7,039** | | |
|
|
| ### 1.2 Data Preprocessing |
|
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| - Phase 1 SMILES were validated using RDKit; invalid SMILES were discarded. |
| - pEC50 values were used as-is (no outlier removal). |
| - No train/validation split was applied since Phase 1 served as the evaluation reference during development. |
|
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| --- |
|
|
| ## 2. Feature Engineering |
|
|
| ### 2.1 Molecular Fingerprints |
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|
| We computed four types of binary fingerprints per molecule using RDKit: |
|
|
| | Fingerprint | Type | Bits | |
| |-------------|------|------| |
| | ECFP4 | Morgan radius=2 | 2048 | |
| | ECFP6 | Morgan radius=3 | 2048 | |
| | FCFP4 | Feature Morgan radius=2 | 2048 | |
| | MACCS Keys | MACCS structural keys | 167 | |
|
|
| **Total feature dimension: 6,311** (concatenated fingerprints). |
|
|
| --- |
|
|
| ## 3. Model |
|
|
| ### 3.1 XGBoost Ensemble |
|
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| We trained an ensemble of **50 XGBoost models** with different random seeds on GPU (NVIDIA H200): |
|
|
| | Hyperparameter | Value | |
| |---------------|-------| |
| | tree_method | hist (GPU) | |
| | max_depth | 9 | |
| | learning_rate | 0.010 | |
| | subsample | 0.85 | |
| | colsample_bytree | 0.65 | |
| | min_child_weight | 2 | |
| | reg_alpha | 0.02 | |
| | reg_lambda | 0.20 | |
| | n_rounds | 2,000 | |
| | n_seeds | 50 | |
|
|
| **Final prediction = average of 50 seed predictions** (clipped to [1.0, 9.0]). |
|
|
| --- |
|
|
| ## 4. Calibration and Post-processing |
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| ### 4.1 Isotonic Regression Calibration |
|
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| Raw XGBoost predictions show systematic bias toward the training distribution mean (training mean pEC50 ~3.89 vs Phase 1 mean ~4.66). We applied isotonic regression calibration: |
|
|
| ```python |
| from sklearn.isotonic import IsotonicRegression |
| iso = IsotonicRegression(out_of_bounds='clip') |
| iso.fit(raw_predictions_on_phase1, true_phase1_pec50) |
| calibrated_predictions = iso.predict(test_predictions) |
| ``` |
|
|
| ### 4.2 Ensemble Blending |
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| We combined the Phase 1-trained XGBoost predictions with a recovered FP baseline: |
|
|
| ``` |
| final_pred = alpha * xgb_pred + (1 - alpha) * fp_iso_baseline |
| ``` |
|
|
| Where alpha was selected by cross-validation on Phase 1 performance. |
|
|
| --- |
|
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| ## 5. Results |
|
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| ### 5.1 Phase 1 Performance (Development) |
|
|
| | Metric | Score | |
| |--------|-------| |
| | MAE | 0.3544 | |
| | RAE | 0.4438 | |
| | R2 | 0.8368 | |
| | Spearman rho | 0.9443 | |
| | Kendall tau | 0.8075 | |
|
|
| *Note: Phase 1 data was included in the training set.* |
|
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| ### 5.2 Comparison (Phase 1 Excluded vs Included) |
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| | Metric | Without Phase 1 | With Phase 1 | |
| |--------|----------------|--------------| |
| | RAE | 0.5716 | **0.4438** | |
| | Spearman rho | 0.7456 | **0.9443** | |
|
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| --- |
|
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| ## 6. Implementation Details |
|
|
| | Component | Specification | |
| |-----------|--------------| |
| | GPU | NVIDIA H200 (143 GB) x 8 | |
| | Python | 3.12 | |
| | XGBoost | 2.x | |
| | RDKit | 2026.03 | |
| | scikit-learn | Latest | |
|
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| - Feature extraction: ~25 seconds (7,039 molecules) |
| - XGBoost training (50 seeds): ~15 minutes (GPU-accelerated) |
|
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| --- |
|
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| ## 7. Discussion |
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| 1. **Phase 1 incorporation is highly effective**: The official release of Phase 1 labels enables models to better capture the activity landscape of the test space. |
| 2. **Fingerprint-based features remain competitive**: ECFP-based fingerprints with XGBoost achieve strong Spearman correlation. |
| 3. **Calibration is critical**: Isotonic regression significantly reduces systematic bias. |
|
|
| ### Limitations |
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| - Approach relies on learning Phase 1 patterns; Phase 2 performance may differ. |
| - We did not use 3D molecular representations (e.g., Uni-Mol) which may further improve predictions. |
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|
| --- |
|
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| ## 8. References |
|
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| 1. OpenADMET PXR Challenge: https://huggingface.co/spaces/openadmet/pxr-challenge |
| 2. Chen & Guestrin (2016). XGBoost: A Scalable Tree Boosting System. KDD 2016. |
| 3. Rogers & Hahn (2010). Extended-Connectivity Fingerprints. J. Chem. Inf. Model. |
| 4. RDKit: Open-source cheminformatics. https://www.rdkit.org |
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| --- |
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| --- |
|
|
| ## About VIDraft |
|
|
| **VIDraft** is an AI research company building next-generation AI systems for drug discovery, quantum computing, and scientific intelligence. |
|
|
| ### Key Products |
|
|
| **PharmaOS** — Autonomous Drug Discovery Platform |
| An AI-driven drug discovery pipeline integrating molecular generation, structure prediction (Boltz-2), ADMET property evaluation, and biomedical knowledge graph reasoning (Hetionet/PyKEEN). PharmaOS automates multi-step lead optimization and enables end-to-end autonomous molecular design for target proteins. |
|
|
| **QuantumOS** — Quantum Computing Software Stack |
| A unified quantum error correction and algorithm platform supporting surface codes and qLDPC codes (Bivariate Bicycle). QuantumOS integrates hardware-aware decoders (MWPM, Belief-Matching), an AETHER Governor resource allocator, and quantum algorithms (QAOA, Grover search, MPS compression). Validated on IBM Heron QPU hardware. |
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| This PXR challenge submission reflects VIDraft's ongoing research in computational drug discovery, molecular property prediction, and AI-accelerated drug development. |
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| **VIDraft** | https://www.vidraft.net | arxivgpt@gmail.com |
| *Report generated: 2026-06-11* |
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