SPCI

Automatic tool for mining structure-property relationships from chemical data sets

Description

Retrieves structure-property relationship from data sets in a chemically meaningful way.
Returns estimated contributions of fragments to the investigated property of compounds from a data set and can estimate contribution of different physicochemical factors as well.

Installation

pip install spci

Features

1. Easy to use straightforward workflow with GUI.
2. Automatic model building and cross-validation.
3. Build models for imbalanced data set using the multiple oversampling approach.
4. Prediction with built models.
5. Several fragmentation schemes to compute fragment contributions of:

• common functional groups and rings;
• Murcko scaffolds;
• user-defined fragments;
• automatically generated fragments (based on SMARTS pattern matching broken bonds);
• per atom fragmentation.

Visualization and analysis of results

1. Built-in visualization.
2. rspci - R package for custom visualization and analysis (https://github.com/DrrDom/rspci)
3. Online tool for visualization, plot customization and figure downloading (http://158.194.101.252:3838/spci-vis/). Demo version is here (http://158.194.101.252:3838/spci-vis-demo/)
4. Per atom contributions can be visualized with RDKit similarity maps.

Manual

The short manual is included.

Citation

1. Polishchuk, P. G.; Kuz'min, V. E.; Artemenko, A. G.; Muratov, E. N., Universal Approach for Structural Interpretation of Qsar/Qspr Models. Mol. Inf. 2013, 32, 843-853 - http://dx.doi.org/10.1002/minf.201300029 - structural interpretation.
2. Polishchuk, P.; Tinkov, O.; Khristova, T.; Ognichenko, L.; Kosinskaya, A.; Varnek, A.; Kuz’min, V., Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis. J. Chem. Inf. Model. 2016, 56, 1455-1469 - http://dx.doi.org/10.1021/acs.jcim.6b00371 - integrated structural and physicochemical interpretation.

Home page

http://qsar4u.com/pages/sirms_qsar.php

License

LGPLv3

What's new

1.0.0 (03.07.2018)

• RDKit is used as a backend instead of Indigo
• multiple undersampling was implemented
• changed default descriptors, that make this version incompatible with previous models and vice versa.
• updated sirms descriptors
• many small fixes and improvements

1.1.0 (07.02.2021)

• added support of RDKit descriptors
• added per atom fragmentation
• reorganized as a Python package
• console scripts have prefix spci_*

1.1.1 (23.03.2021)

• changed license to LGPLv3
• fixed arguments in scpi_descriptors

1.1.2 (28.06.2023)

• add max_size argument to find_frag_auto_rdkit.py to limit maximum size of output fragments
• skip fragments with H as a context from output of find_frag_auto_rdkit.py
• update README and installation notes

1.1.4 (04.11.2024)

• fix calculation of fragment contributions if initial molecules do not contain hydrogens

1.1.5 (17.01.2026)

• adjust fingerprint generation to use novel RDKit generators
• adjust undersampling to use the recent numpy version
• add support of SMILES input in descriptors.py