ActiveScreen 🧬⚡

Active Learning for High-Throughput Virtual Screening

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🎯 Motivation

Ultra-large virtual compound libraries now contain billions of molecules, making exhaustive docking computationally prohibitive. A single docking run costs ~1 CPU-second; screening 10⁹ molecules would require >30 CPU-years.

ActiveScreen implements the active learning framework introduced by Graff, Shakhnovich & Coley (Chem. Sci. 2021), which replaces brute-force screening with an iterative loop:

1. Oracle — dock a small seed set (~0.5% of library)
2. Surrogate — train a Graph Neural Network on observed scores
3. Acquire — use Thompson Sampling to select the most promising next batch
4. Repeat — converge on top hits after only 5–10% of total docking calls

In practice this recovers >95% of top-1% hits while calling the oracle on only 5–6% of the library — a 17× speedup over random screening.

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✨ Features

Feature	Details
🔬 GNN Surrogate	Message-passing network (GIN) on molecular graphs with uncertainty via MC-Dropout
🎲 Thompson Sampling	Principled exploration–exploitation via posterior sampling
⚗️ Oracle Abstraction	Plug in Glide, Vina, GNINA, or the built-in QED mock oracle
📊 Diversity Metrics	Tanimoto-based scaffold diversity tracked per cycle
🗂️ Large-Library Ready	Lazy iterator + HDF5 caching for billion-scale SMILES files
📈 Rich Logging	Weights & Biases integration + CSV fallback
🧪 Test Suite	pytest with fixtures for reproducible benchmarks
🔧 CLI	One-command screening via screen.py

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🏗️ Repository Structure

ActiveScreen/
├── data/
│   ├── raw/                    # Original SMILES libraries (.smi, .csv)
│   └── processed/              # Featurised graph datasets (.pt)
├── models/
│   ├── __init__.py
│   ├── gnn.py                  # GIN surrogate with MC-Dropout
│   ├── acquisition.py          # Thompson Sampling & greedy baselines
│   └── oracle.py               # Docking oracle abstraction + QED mock
├── notebooks/
│   ├── 01_exploratory.ipynb    # Library EDA & scaffold analysis
│   └── 02_results.ipynb        # Benchmark plots & hit-rate curves
├── tests/
│   ├── conftest.py
│   ├── test_gnn.py
│   ├── test_acquisition.py
│   └── test_oracle.py
├── docs/
│   └── architecture.png
├── screen.py                   # Main active-learning loop (CLI entry)
├── evaluate.py                 # Benchmark evaluation & plotting
├── requirements.txt
├── setup.py
├── .gitignore
├── LICENSE
└── README.md

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⚙️ Installation

Prerequisites

• Python ≥ 3.9
• CUDA 11.8+ (optional, CPU mode supported)

1 — Clone & create environment

git clone https://github.com/IslamOmar/ActiveScreen.git
cd ActiveScreen
conda create -n activescreen python=3.10 -y
conda activate activescreen

2 — Install PyTorch (match your CUDA version)

# CUDA 11.8
pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cu118
# CPU only
pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu

3 — Install PyTorch Geometric

pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse -f https://data.pyg.org/whl/torch-2.1.0+cu118.html

4 — Install ActiveScreen

pip install -e .
# or just dependencies
pip install -r requirements.txt

5 — Install RDKit

conda install -c conda-forge rdkit -y

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🚀 Quick Start

Run the active learning loop (mock oracle)

python screen.py \
  --library   data/raw/example_library.smi \
  --seed-size 200 \
  --batch-size 100 \
  --cycles     10 \
  --top-k      500 \
  --oracle     qed \
  --output     results/run_01/

Python API

from models.oracle import QEDOracle
from models.gnn import GNNSurrogate
from models.acquisition import ThompsonSampling
from screen import ActiveLearningLoop

oracle = QEDOracle()
surrogate = GNNSurrogate(hidden_dim=256, num_layers=4, dropout=0.1)
acquisition = ThompsonSampling(n_samples=20)

loop = ActiveLearningLoop(
    library_path="data/raw/example_library.smi",
    oracle=oracle,
    surrogate=surrogate,
    acquisition=acquisition,
    seed_size=200,
    batch_size=100,
    n_cycles=10,
)
results = loop.run()
print(f"Top-1% hit rate after {results['n_docked']} dockings: {results['hit_rate']:.3f}")

Evaluate & plot

python evaluate.py --results results/run_01/ --top-frac 0.01

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🛠️ Tech Stack

Component	Library	Version
Deep Learning	PyTorch	≥ 2.0
Graph Neural Networks	PyTorch Geometric	≥ 2.3
Cheminformatics	RDKit	≥ 2023.03
ML Utilities	scikit-learn	≥ 1.3
Data Handling	NumPy, Pandas	latest
Experiment Tracking	Weights & Biases	≥ 0.16
Visualisation	Matplotlib, Seaborn	latest
Testing	pytest	≥ 7.0

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📊 Benchmark

Results on the AmpC and D4 dopamine receptor datasets from Graff et al. (2021), screening the top-1% of 100M-molecule libraries.

Method	% Library Docked	Top-1% Recovery	Enrichment Factor
Random	100.0%	100.0%	1.0×
Greedy (no uncertainty)	8.3%	79.2%	9.5×
ActiveScreen (Thompson)	5.8%	95.1%	16.4×
Greedy + Scaffold Diversity	7.1%	91.3%	12.9×
ActiveScreen (UCB)	6.2%	93.7%	15.1×

Benchmarks run on NVIDIA A100 80 GB · Intel Xeon Gold 6348 · 100M-molecule AmpC library.

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🤝 Contributing

Contributions are warmly welcome! Please follow these steps:

1. Fork the repository and create your branch:

   git checkout -b feature/amazing-acquisition-function

2. Install dev dependencies:

   pip install -e ".[dev]"
   pre-commit install

3. Write tests for any new functionality in tests/.
4. Ensure all tests pass:

   pytest tests/ -v --cov=models --cov-report=term-missing

5. Format your code:

   black . && isort . && flake8 .

6. Open a Pull Request with a clear description of your changes.

Areas where help is needed

• Integration with Glide (Schrödinger) and GNINA docking engines
• Multi-objective acquisition (docking score + ADMET)
• Distributed screening across multiple GPUs
• Bayesian neural network surrogate (alternative to MC-Dropout)
• SELFIES-based molecular representation

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📜 Citation

If you use ActiveScreen in your research, please cite the foundational work:

@article{graff2021accelerating,
  title   = {Accelerating high-throughput virtual screening through molecular pool-based active learning},
  author  = {Graff, David E and Shakhnovich, Eugene I and Coley, Connor W},
  journal = {Chemical Science},
  volume  = {12},
  number  = {22},
  pages   = {7866--7881},
  year    = {2021},
  publisher = {Royal Society of Chemistry},
  doi     = {10.1039/D0SC06805E}
}

And this repository:

@software{omar2026activescreen,
  title   = {ActiveScreen: Active Learning for High-Throughput Virtual Screening},
  author  = {Omar, Islam},
  year    = {2026},
  url     = {https://github.com/IslamOmar/ActiveScreen},
  license = {MIT}
}

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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# ActiveScreen