Metadata-Version: 2.4
Name: pyCSRML
Version: 0.4
Summary: Unofficial Python implementation of CSRML chemotype fingerprints (ToxPrint v2 and TxP_PFAS v1).
Project-URL: Homepage, https://github.com/LucMiaz/pyCSRML
Project-URL: Documentation, https://pycsrml.readthedocs.io
Project-URL: Repository, https://github.com/LucMiaz/pyCSRML
Project-URL: Bug Tracker, https://github.com/LucMiaz/pyCSRML/issues
Project-URL: Changelog, https://github.com/LucMiaz/pyCSRML/blob/main/CHANGELOG.md
Author-email: Luc Miaz <luc@miaz.ch>
License: Attribution 4.0 International
        
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License-File: LICENSE
Keywords: CSRML,PFAS,RDKit,SMARTS,ToxPrint,cheminformatics,chemotype,fingerprint
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Requires-Python: >=3.10
Requires-Dist: numpy>=1.23
Requires-Dist: rdkit>=2022.3
Provides-Extra: analysis
Requires-Dist: matplotlib>=3.6; extra == 'analysis'
Requires-Dist: pandas>=1.5; extra == 'analysis'
Provides-Extra: dev
Requires-Dist: matplotlib>=3.6; extra == 'dev'
Requires-Dist: myst-parser; extra == 'dev'
Requires-Dist: pandas>=1.5; extra == 'dev'
Requires-Dist: pylint>=3; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: pytest>=7; extra == 'dev'
Requires-Dist: sphinx-autodoc-typehints; extra == 'dev'
Requires-Dist: sphinx-rtd-theme>=2; extra == 'dev'
Requires-Dist: sphinx>=7; extra == 'dev'
Provides-Extra: full
Requires-Dist: matplotlib>=3.6; extra == 'full'
Requires-Dist: pandas>=1.5; extra == 'full'
Requires-Dist: pyyaml>=6; extra == 'full'
Requires-Dist: umap-learn>=0.5; extra == 'full'
Provides-Extra: umap
Requires-Dist: umap-learn>=0.5; extra == 'umap'
Provides-Extra: yaml
Requires-Dist: pyyaml>=6; extra == 'yaml'
Description-Content-Type: text/markdown

# pyCSRML

[![CI](https://github.com/LucMiaz/pyCSRML/actions/workflows/ci.yml/badge.svg)](https://github.com/LucMiaz/pyCSRML/actions/workflows/ci.yml)
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[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Docs](https://readthedocs.org/projects/pycsrml/badge/?version=latest)](https://pycsrml.readthedocs.io)

**pyCSRML** is a pure-Python re-implementation of the
[Chemical Subgraph Representation Markup Language (CSRML)](https://www.molecular-networks.com/products/chemotyper).
It parses CSRML XML files, converts the subgraph patterns to SMARTS, and computes
binary chemotype fingerprints for molecules using [RDKit](https://www.rdkit.org/).

The module is not an exact replicate of the original CSRML (see [performance section](#performance)). the original software should be preferred.

The module was implemented from two fingerprints descriptions:

| Fingerprint | Bits | Description | Sourcde |
|---|---|---|----|
| **ToxPrint v2.0** | 729 | General toxicologically relevant substructures | Yang *et al.* 2015  |
| **TxP_PFAS v1.0** | 129 | Per- and polyfluoroalkyl substance (PFAS) chemotypes | Richard *et al.* 2023



## Performance

Accuracy is measured by comparing pyCSRML bit vectors against the reference
[ChemoTyper](https://www.molecular-networks.com/products/chemotyper) tool output.
Run `pytest tests/test_chemotyper_concordance.py -v -s` to reproduce; the full
per-bit breakdown is written to `tests/concordance_report.md`.

| Dataset | Compounds | Fingerprint | Overall accuracy | Bits ≥ 90 % acc | Macro MCC | Macro Bal Acc | Macro ROC-AUC |
|---|---|---|---|---|---|---|---|
| Richard *et al.* 2023 (PFAS set) | 14 710 | TxP_PFAS v1 | **99.99 %** | 129 / 129 | **0.9971** | **0.9989** | **0.9989** |
| ToxCast (full) | 9 014 | ToxPrint v2 | **99.71 %** | 725 / 729 | **0.9326** | **0.9703** | **0.9703** |
| ToxCast (CF-containing subset) | 808 | TxP_PFAS v1 | **99.98 %** | 129 / 129 | **0.9905** | **0.9924** | **0.9924** |
| CLinventory | 181 745 | ToxPrint v2 | **99.77 %** | 726 / 729 | **0.9320** | **0.9710** | **0.9710** |
| CLinventory | 181 745 | TxP_PFAS v1 | **100.00 %** | 129 / 129 | **0.9936** | **0.9946** | **0.9946** |

> **Reading the table:** "CF-containing subset" means only the 808 ToxCast compounds
> for which ChemoTyper sets at least one TxP_PFAS bit — the meaningful subset for
> PFAS accuracy benchmarking. Full-dataset TxP_PFAS accuracy appears inflated (100 %)
> because the vast majority of compounds are all-zero for every PFAS bit.

### Known discrepancies

The 4 bits below 90 % accuracy in ToxPrint v2 are in ring heteroatom and chain
chemotype groups; TxP_PFAS v1 has 3 bits below 100 % (all above 98.9 %).  
Root causes (see `tests/concordance_report.md`):

| Bit / category | Fingerprint | Accuracy | Direction | Root cause |
|---|---|---|---|---|
| `ring:hetero_[6]_Z_generic` | ToxPrint | 54.6 % | False positives | Over-broad 6-membered heteroatom-ring SMARTS; pyCSRML prevalence 69.6 % vs ChemoTyper 24.3 % |
| `chain:alkaneBranch_isopropyl_C3` | ToxPrint | 74.1 % | False positives | Ring-attachment SMARTS permissive on `noZ` (not-connected-to-heteroatom) modifier; pyCSRML prevalence 37.5 % vs ChemoTyper 11.6 % |
| `chain:alkaneCyclic_ethyl_C2_(connect_noZ)` | ToxPrint | 75.9 % | False positives | Same `noZ` over-matching; pyCSRML prevalence 41.8 % vs ChemoTyper 17.7 % |
| `chain:alkeneCyclic_ethene_generic` | ToxPrint | 87.0 % | False positives | Cyclic alkene SMARTS over-matches; pyCSRML prevalence 17.4 % vs ChemoTyper 10.3 % |
| `pfas_chain:alkeneLinear_mono-ene_ethylene_generic_F` | TxP_PFAS | 98.9 % | False negatives (recall 40 %) | RDKit perceives the C=C of tautomeric fluoropyrimidines (5-fluorouracil) as aromatic; the SMARTS `[#9]-[#6;A]=[#6;A]` requires aliphatic atoms and misses them |
| `pfas_bond:C=N_imine_FCN` | TxP_PFAS | 99.5 % | False negatives (recall 33 %) | Same aromaticity issue: the C=N bond in fluorinated heterocycles is perceived as aromatic by RDKit, so the aliphatic imine SMARTS does not match |
| `pfas_bond:aromatic_FCc1c` | TxP_PFAS | 99.5 % | Slight false positives (precision 97.2 %) | Aromatic F-C pattern slightly over-matches due to SMARTS approximation of the exception clause |

---

## Timing Benchmark

Five molecule-size-stratified sets are extracted from the CLinventory and used
to compare pyCSRML speed against ChemoTyper on realistic chemical diversity.

| Set | Heavy-atom range | Molecules |
|---|---|---|
| `bench_tiny` | 1 – 10 | *auto* |
| `bench_small` | 11 – 20 | *auto* |
| `bench_medium` | 21 – 35 | *auto* |
| `bench_large` | 36 – 60 | *auto* |
| `bench_xlarge` | 61 + | *auto* |

### Timing results (ms / molecule)

| Set | Heavy atoms | pyCSRML ToxPrint v2 | pyCSRML TxP\_PFAS v1 | ChemoTyper ToxPrint v2 | ChemoTyper TxP\_PFAS v1 |
|---|---|---|---|---|---|
| bench\_tiny | 1 – 10 | **3.76** | **0.73** | 13.83 | 4.29 |
| bench\_small | 11 – 20 | **5.47** | **1.01** | 27.70 | 7.74 |
| bench\_medium | 21 – 35 | **8.23** | **1.53** | 59.63 | 17.87 |
| bench\_large | 36 – 60 | **12.32** | **2.19** | 114.64 | 30.53 |
| bench\_xlarge | 61 + | **23.20** | **4.46** | 322.33 | 139.09 |

pyCSRML measured on **Snapdragon X Elite X1E78100** (ARM64, 12 cores, ~32 GB RAM),
Python 3.14.2, RDKit 2025.09.3, NumPy 2.3.5; 5 repetitions, median reported.
ChemoTyper timings measured manually on the same machine, 3 repetitions, mean reported;
values are of limited precision due to the manual measurement procedure.
500 molecules per set.

### How to reproduce

**1. Extract benchmark sets** (one-time):

```bash
python scripts/create_size_benchmarks.py
```

Outputs `tests/test_data/size_benchmarks/bench_*.smiles`,
`bench_metadata.csv`, and `chemotyper_timing_template.csv`.

**2. Time pyCSRML** (saves `pycsrml_timing_baseline.json`):

```bash
python scripts/benchmark_pycsrml_timing.py          # 5 reps by default
python scripts/benchmark_pycsrml_timing.py --reps 3 # faster
```

**3. Run ChemoTyper** on each `.smiles` file (ToxPrint V2 and TxP\_PFAS v1),
export results as TSV, and place zips in `tests/test_data/size_benchmarks/`:

```
bench_tiny_toxprint.zip    bench_tiny_txppfas.zip
bench_small_toxprint.zip   bench_small_txppfas.zip
...
```

Fill in the three-repetition ChemoTyper timing in `chemotyper_timing_template.csv`.

**4. Run regression tests**:

```bash
pytest tests/test_benchmark_regression.py -v -m slow
```

Timing regression: fails if any set is >30 % slower than the saved baseline.  
Accuracy regression: fails if overall bit accuracy drops >0.1 pp from baseline.
Both test types skip gracefully until their respective baseline / zip files exist.

---

## Installation

The module needs RDKit installed. If necessary, start by installing a environment manager first (e.g. Conda/Mamba, like [Miniforge3](https://github.com/conda-forge/miniforge)) and creating an environment, e.g.:

```bash
mamba create -n rdkit pytho
mamba activate rdkit
mamba install -y -c rdkit rdkit
```

Then install pyCSRML via PyPI:

```bash
pip install pyCSRML
```


---

## Quick start

### Single molecule (ToxPrint v2.0, 729 bits)

```python
from pyCSRML import Fingerprinter, TOXPRINT_PATH
from rdkit import Chem

fp = Fingerprinter(TOXPRINT_PATH)

mol = Chem.MolFromSmiles("c1ccccc1")   # benzene
arr, names = fp.fingerprint(mol)

print(f"Bits set: {arr.sum()} / {fp.n_bits}")
on_bits = [names[i] for i in range(len(arr)) if arr[i]]
print(on_bits[:5])
```

### TxP\_PFAS fingerprints (129 bits)

```python
from pyCSRML import Fingerprinter, TXPPFAS_PATH
from rdkit import Chem

fp = Fingerprinter(TXPPFAS_PATH)
mol = Chem.MolFromSmiles("FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)O")  # PFOA
arr, names = fp.fingerprint(mol)
print(f"Bits set: {arr.sum()} / {fp.n_bits}")
```

### Batch fingerprinting

```python
from pyCSRML import Fingerprinter, TXPPFAS_PATH
from rdkit import Chem

mols = [Chem.MolFromSmiles(s) for s in smiles_list]
fp = Fingerprinter(TXPPFAS_PATH)
matrix = fp.fingerprint_batch(mols)   # shape (n_mols, 129), dtype bool
print(matrix.shape)
```

### Low-level CSRML parsing

```python
from pyCSRML._csrml import parse_csrml_xml, ordered_bit_list

data = parse_csrml_xml("path/to/my_fingerprints.xml")
bits = ordered_bit_list(data)
for bit in bits[:3]:
    print(bit["id"], bit["smarts"])
```

---

## API overview

| Symbol | Module | Description |
|---|---|---|
| `Fingerprinter` | `pyCSRML` | Compute fingerprints from any CSRML XML, JSON, or YAML definition |
| `TOXPRINT_PATH` | `pyCSRML` | Path to the bundled ToxPrint v2.0 JSON (729 bits) |
| `TXPPFAS_PATH` | `pyCSRML` | Path to the bundled TxP\_PFAS v1.0.4 JSON (129 bits) |
| `parse_csrml_xml` | `pyCSRML._csrml` | Parse raw CSRML XML → Python dict |
| `ordered_bit_list` | `pyCSRML._csrml` | Return all bits in order from a parsed dict |

Full API reference: **[pycsrml.readthedocs.io](https://pycsrml.readthedocs.io)**

---

## CSRML features supported

| Feature | Status |
|---|---|
| `substructureMatch` → SMARTS | ✅ Full |
| `substructureException` (global) | ✅ Full |
| `matchingQueryAtom` → `[!$(...)]` folding | ✅ Full |
| `combineAtomFeatures` (OR-of-AND trees) | ✅ Full |
| `atomList` with `negate="true"` | ✅ Full |
| `attachedHydrogenCount` ranges | ✅ Full |
| `ringCountAtom` / `ringAtom` / `chainAtom` | ✅ Full |
| Pseudo-elements G, Z, Q, X | ✅ Full |
| `mustMatch` / `mustNotMatch` (test cases) | parsed, not used for matching |

---

## Development

```bash
git clone https://github.com/LucMiaz/pyCSRML
cd pyCSRML
pip install -e ".[dev]"

# Run tests (fast)
pytest -m "not slow"

# Run concordance test (~45 s)
pytest tests/test_chemotyper_concordance.py -v -s

# Pylint
pylint pyCSRML/
```

---

## Citation

If you use pyCSRML in academic work, please cite the original ToxPrint / ChemoTyper paper and the TxP_PFAS reference:

- Yang, C., *et al.* (2015). New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modelling. *J. Chem. Inf. Model.* **55**, 510–528.
- Richard, A.M., *et al.* (2023). ToxPrint chemotypes and ChemoTyper portal. *Chem. Res. Toxicol.* **36**, 488–510.

---

## Licence
<a href="https://github.com/LucMiaz/pyCSRML">pyCSRML</a> © 1999 by <a href="https://cogitopia.dev">Luc T. Miaz</a> is licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a><img src="https://mirrors.creativecommons.org/presskit/icons/cc.svg" alt="" style="max-width: 1em;max-height:1em;margin-left: .2em;"><img src="https://mirrors.creativecommons.org/presskit/icons/by.svg" alt="" style="max-width: 1em;max-height:1em;margin-left: .2em;">

## Acknowledgments
This project is part of the [ZeroPM project](https://zeropm.eu/) (WP2) and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036756. This work was developed at the [Department of Environmental Science](https://aces.su.se) at Stockholm University.<br />


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