Based on the publication:

Orlov, A. A., Akhmetshin, T. N., Horvath, D., Marcou, G., & Varnek, A. "From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization." Molecular Informatics, 2024, 44(1). DOI: 10.1002/minf.202400265

Installation

Requires Python 3.11 and uv.

git clone https://github.com/AxelRolov/cdr_bench.git
cd cdr_bench
uv sync

Quick Usage

# 1. Generate molecular descriptors from SMILES
python scripts/generate_descriptors.py bench_configs/features.toml

# 2. Run benchmarking (grid search optimization)
python scripts/run_benchmarking.py --config bench_configs/run_benchmarking.toml

# 3. Analyze and aggregate results
python scripts/analyze_results.py --input_dir results/ --output_dir results/ --k_hit 20

Documentation

Full documentation is available at axelrolov.github.io/cdr_bench.

Project Structure

cdr_bench/
├── src/cdr_bench/          # Core library
│   ├── dr_methods/         # DimReducer wrapper (PCA, UMAP, t-SNE, GTM)
│   ├── optimization/       # Grid search optimizer and parameter definitions
│   ├── scoring/            # Quality metrics (NN overlap, co-ranking, trustworthiness)
│   ├── io_utils/           # HDF5 I/O, config loading, data preprocessing
│   ├── features/           # Descriptor generation (Morgan FP, MACCS, ChemDist)
│   └── visualization/      # Plotting utilities
├── scripts/                # Pipeline scripts
│   ├── run_benchmarking.py
│   ├── generate_descriptors.py
│   ├── analyze_results.py
│   ├── prepare_lolo.py
│   └── analyze_lib_distance_preservation.py
├── bench_configs/          # TOML configuration files
│   ├── run_benchmarking.toml
│   ├── features.toml
│   └── method_configs/     # Per-method hyperparameter grids
├── datasets/               # Sample ChEMBL datasets (HDF5)
├── results/                # Benchmark results and metrics
├── notebooks/              # Jupyter notebooks for analysis
└── tests/                  # Test suite

Datasets

The datasets/ directory contains ChEMBL subset datasets used in the study. Full datasets and all embeddings are available on Zenodo.

Citation

@article{orlov2024high,
  title={From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization},
  author={Orlov, Alexey A. and Akhmetshin, Tagir N. and Horvath, Dragos and Marcou, Gilles and Varnek, Alexandre},
  journal={Molecular Informatics},
  volume={44},
  number={1},
  pages={e202400265},
  year={2024},
  doi={10.1002/minf.202400265}
}

Generative Topographic Mapping

The GTM results in the original publication were obtained using an in-house implementation. This repository uses the open-source ChemographyKit for GTM. If you use it, please cite the ChemographyKit publication as well.

License

MIT