---
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

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.

---

## 1. Data

### 1.1 Training Data

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

- 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.

---

## 2. Feature Engineering

### 2.1 Molecular Fingerprints

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

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

### 4.1 Isotonic Regression Calibration

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

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.

---

## 5. Results

### 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.*

### 5.2 Comparison (Phase 1 Excluded vs Included)

| Metric | Without Phase 1 | With Phase 1 |
|--------|----------------|--------------|
| RAE | 0.5716 | **0.4438** |
| Spearman rho | 0.7456 | **0.9443** |

---

## 6. Implementation Details

| Component | Specification |
|-----------|--------------|
| GPU | NVIDIA H200 (143 GB) x 8 |
| Python | 3.12 |
| XGBoost | 2.x |
| RDKit | 2026.03 |
| scikit-learn | Latest |

- Feature extraction: ~25 seconds (7,039 molecules)
- XGBoost training (50 seeds): ~15 minutes (GPU-accelerated)

---

## 7. Discussion

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

- 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.

---

## 8. References

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

---

---

## 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.

This PXR challenge submission reflects VIDraft's ongoing research in computational drug discovery, molecular property prediction, and AI-accelerated drug development.

**VIDraft** | https://www.vidraft.net | arxivgpt@gmail.com  
*Report generated: 2026-06-11*