Py-CoMSIA: An Open-Source Implementation of Comparative Molecular Similarity Indices Analysis in Python

Py-CoMSIA: An Open-Source Implementation of Comparative Molecular Similarity Indices Analysis in Python

<b>Background/Objectives:</b> The progression of three-dimensional (3D) quantitative structure–activity relationship (QSAR) methodologies has significantly contributed to the advancement of medicinal chemistry and pharmaceutical discovery. Comparative Molecular Similarity Indices Analysi...

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Bibliographic Details
Main Authors: Christopher L. Haga, Crystal N. Le, Xue D. Yang, Donald G. Phinney
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Pharmaceuticals
Subjects:
CoMSIA
Python
3D QSAR
grid-based QSAR
Online Access:https://www.mdpi.com/1424-8247/18/3/440
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Summary:<b>Background/Objectives:</b> The progression of three-dimensional (3D) quantitative structure–activity relationship (QSAR) methodologies has significantly contributed to the advancement of medicinal chemistry and pharmaceutical discovery. Comparative Molecular Similarity Indices Analysis (CoMSIA) is a widely used 3D-QSAR technique. However, its reliance on discontinued proprietary software creates accessibility challenges. This work aims to develop an open-source Python library to address these limitations and broaden access to grid-based 3D-QSAR methods. <b>Methods:</b> Py-CoMSIA was developed in Python using RDKit and NumPy for calculations and PyVista for visualizations. <b>Results:</b> Py-CoMSIA provides a functional open-source alternative to proprietary CoMSIA software. It successfully implements the core CoMSIA algorithm and generates comparable similarity indices, as demonstrated by testing several benchmarking datasets including the original CoMSIA steroid dataset. <b>Conclusions:</b> The Py-CoMSIA library addresses the accessibility issues associated with proprietary 3D-QSAR software by providing an open-source Python implementation of CoMSIA. This tool broadens access to complex grid-based 3D-QSAR methodologies and offers a flexible platform for integrating advanced statistical and machine learning techniques.
ISSN:1424-8247

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