# QRCI

**Repository Path**: drugai_plus/QRCI

## Basic Information

- **Project Name**: QRCI
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-20
- **Last Updated**: 2025-06-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: qeppi, qrci

## README

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow)](https://github.com/AspirinCode/QRCI)
[![ChemRxiv2025](https://img.shields.io/badge/ChemRxiv-10.26434%2Fchemrxiv--2025--mlqwl--v2-green)](https://doi.org/10.26434/chemrxiv-2025-mlqwl-v2)
[![PyPI](https://img.shields.io/badge/PyPI-cyan)](https://pypi.org/project/qrci)

# QRCI

**A Quantitative Ring Complexity Index for Profiling Ring Topology and Chemical Diversity** 

![QRCI](https://gitee.com/drugai_plus/QRCI/blob/main/figures/qrci_cover.png)

### Quantitative Ring Complexity Index

$\mathrm{QRCI}=\frac{\mathrm{TRS}}{N_{\mathrm{ra}}}\left(1+\frac{N_{\mathrm{fr}}}{N_{\mathrm{r}}+1}\right)+\sum_{r}\left[\frac{360}{360-\alpha_{\mathrm{ideal}}(\ell_{r})}\cdot\frac{1}{\ell_{r}}\cdot\lambda_{M}(\ell_{r})\right]+\frac{\sum W_{i}\cdot D_{i}}{\sqrt{N_{\mathrm{ra}}\cdot\mathrm{TRS}}}+\frac{\log(N_{\mathrm{ta}})}{N_{\mathrm{r}}+1}+W_{m}\cdot\frac{N_{\mathrm{mr}}}{N_{\mathrm{r}}+1}$  

* **TRS** (Total Ring Size): Sum of all ring sizes.
* **$N_{\mathrm{ra}}$**: Total number of atoms in all rings.
* **$N_{\mathrm{r}}$**: Total number of rings
* **$N_{\mathrm{fr}}$** (Fused Rings): Count of rings sharing atoms or bonds.
* **$N_{\mathrm{ta}}$**: Total number of atoms
* **$N_{\mathrm{mr}}$**: total number of macrocycles
* **$W_{m}$**: Weight for macrocycle descriptors.
* **$W_{i}$**: Weight for topological descriptors.
* **$D_{i}$**: Topological ring diversity descriptor.


### Ring Complexity Index
$RCI=\frac{TRS}{nRingAtoms}$  
where TRS is the total ring size and $nRingAtoms$ is the number of atoms belonging to a ring.
Ref: Gasteiger, J., & Jochum, C. (1979). An Algorithm for the Perception of Synthetically Important Rings. Journal of Chemical Information and Computer Sciences, 19(1), 43–48. https://doi.org/10.1021/ci60017a011  

![RCI of Drug](https://github.com/AspirinCode/QRCI/blob/main/figures/drugbank5.1.13_apvd_r1r10w900_rci_histdist.png)
**Distribution of RCI for approved drugs of DrugBank**

## Requirements
```python
Python==3.13.2
rdkit==2025.03.2
scipy==1.15.1
```


## How to install the tool

QRCI can be installed from pypi ([https://pypi.org/project/qrci](https://pypi.org/project/qrci)).

```python
pip install qrci
```

### Usage

```python
from QRCI.QRCI import QRCICalculator, get_QRCIproperties
from QRCI.RCI import RCICalculator

qrci_calc = QRCICalculator(weights='mean')
score_mean = qrci_calc('C1=CCOCc2cc(ccc2OCCN2CCCC2)Nc2nccc(n2)-c2cccc(c2)COC1')
print(f"QRCI(default/mean weights): {score_mean:.4f}")
#QRCI(default/mean weights): 4.0330

***************************************************************************************
mol = Chem.MolFromSmiles('C1=CCOCc2cc(ccc2OCCN2CCCC2)Nc2nccc(n2)-c2cccc(c2)COC1')
props = get_qrci_properties(mol)
print(props)
#QRCIproperties(nAromHetero=1, nAromCarbo=2, nAliHetero=2, nAliCarbo=0, nSatHetero=1, nSatCarbo=0, nMacrocycles=1, TRS=41, nRingAtom=32, nFusedRing=4, SF=1.0857142857142856)
```

## Data

* [DrugBank](https://go.drugbank.com/)  
* [iPPI-DB](https://ippidb.pasteur.fr/)  
* [COCONUT: the COlleCtion of Open NatUral producTs](https://coconut.naturalproducts.net/)  
* [ChEMBL35](https://www.ebi.ac.uk/chembl/)
* [PubChem](https://pubchem.ncbi.nlm.nih.gov/)  


### Molecular Standardization

https://www.rdkit.org/docs/source/rdkit.Chem.MolStandardize.rdMolStandardize.html

https://github.com/rdkit/rdkit/blob/master/Docs/Notebooks/MolStandardize.ipynb



## QRCI calculation
[QRCI/QRCI_calculate_v1.1.ipynb](https://github.com/AspirinCode/QRCI/blob/main/QRCI/QRCI_calculate_v1.1.ipynb)  

Example:[Pacritinib](https://go.drugbank.com/drugs/DB11697)  


```python
qrci_calc = QRCICalculator(weights='mean')
score_mean = qrci_calc('C1=CCOCc2cc(ccc2OCCN2CCCC2)Nc2nccc(n2)-c2cccc(c2)COC1')
print(f"QRCI(default/mean weights): {score_mean:.4f}")
#QRCI(default/mean weights): 4.0330

***************************************************************************************
mol = Chem.MolFromSmiles('C1=CCOCc2cc(ccc2OCCN2CCCC2)Nc2nccc(n2)-c2cccc(c2)COC1')
props = get_qrci_properties(mol)
print(props)
#QRCIproperties(nAromHetero=1, nAromCarbo=2, nAliHetero=2, nAliCarbo=0, nSatHetero=1, nSatCarbo=0, nMacrocycles=1)

```

![RCI of Drug](https://github.com/AspirinCode/QRCI/blob/main/figures/drugbank5.1.13_apvd_r1r10w900_qrci_histdist.png)
**Distribution of QRCI for approved drugs of DrugBank**

## Analysis

### Spacial Score

```python
rdkit.Chem.SpacialScore.SPS(mol, normalize=True)
```

https://rdkit.org/docs/source/rdkit.Chem.SpacialScore.html  

https://github.com/frog2000/Spacial-Score  


### SAscore

```python
#Calculating SAscore
import sascorer
sascore = sascorer.calculateScore()
```

https://greglandrum.github.io/rdkit-blog/posts/2023-12-01-using_sascore_and_npscore.html


### QED

```python
from rdkit import Chem
from rdkit.Chem import QED

smiles = "C=CCN1CC(C(=O)N(CCCN(C)C)C(=O)NCC)C[C@@H]2c3cccc4[nH]cc(c34)C[C@H]21"
mol = Chem.MolFromSmiles(smiles)

qed_score = QED.qed(mol)
print(f"QED Score: {qed_score:.3f}")
#QED Score: 0.605
```

https://www.rdkit.org/docs/source/rdkit.Chem.QED.html#module-rdkit.Chem.QED


### QEPPI
quantitative estimate of protein-protein interaction targeting drug-likeness  

```python
#Calculates QEPPI
q = ppi.QEPPI_Calculator()
print("QEPPI.model LOADING...")
q.load("./QEPPI/QEPPI.model")

smiles = "C=CCN1CC(C(=O)N(CCCN(C)C)C(=O)NCC)C[C@@H]2c3cccc4[nH]cc(c34)C[C@H]21"
mol = Chem.MolFromSmiles(smiles)
print(q.qeppi(mol))
```

https://github.com/ohuelab/QEPPI  



### Others

**Drug Data From the ChEMBL**

https://github.com/PatWalters/practical_cheminformatics_tutorials/tree/main/misc

![RCI/QRCI of Drugs by Era](https://github.com/AspirinCode/QRCI/blob/main/figures/drug2025_by_year_rci_qrci_Trend_1year_dist.png)
**Trend of RCl/QRCl Over Time (approved drugs of ChEMBL 35)**

## License
Code is released under MIT LICENSE.


## Cite

* Gasteiger, J. and Jochum, C., 1979. An algorithm for the perception of synthetically important rings. Journal of Chemical Information and Computer Sciences, 19(1), pp.43-48.
* Ertl, P., Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1, 8 (2009). https://doi.org/10.1186/1758-2946-1-8
* Krzyzanowski, A., Pahl, A., Grigalunas, M., & Waldmann, H. (2023). Spacial Score─A Comprehensive Topological Indicator for Small-Molecule Complexity. Journal of medicinal chemistry, 66(18), 12739–12750. https://doi.org/10.1021/acs.jmedchem.3c00689
* Wang J, Xu K, Ma T, Zhang X, Ma P, Li  C, et al. A Quantitative Ring Complexity Index for Profiling Ring Topology and Chemical Diversity. ChemRxiv. 2025; doi:[10.26434/chemrxiv-2025-mlqwl-v2](https://doi.org/10.26434/chemrxiv-2025-mlqwl-v2)  This content is a preprint and has not been peer-reviewed.