QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices

Year : 2025 | Volume : 03 | Issue : 01 | Page : 50-57
    By

    Sheshmani Tiwari,

  1. Student, Department of Bio Chemical Engineering, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

Abstract

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Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and AI-based techniques has significantly improved predictive accuracy and the handling of large datasets. This review outlines the key stages of QSAR model development, including data collection, descriptor selection, algorithm choice, model validation, and result interpretation. Emphasis is placed on dataset quality, reproducibility, and clearly defining a model’s applicability domain. The review also examines popular QSAR software—both commercial and open-source—that streamline model creation and evaluation. Regulatory guidelines, such as those from the OECD, are discussed to highlight best practices for ensuring model reliability in regulatory contexts. Emerging innovations like deep learning, transfer learning, and generative models are also explored. The article concludes with a discussion of current challenges and future directions, aiming to support researchers in developing robust QSAR models for chemical safety and pharmaceutical applications.

Keywords: Artificial Intelligence, chemical safety, machine learning, QSAR modeling, toxicity prediction

[This article belongs to International Journal of Cheminformatics ]

How to cite this article:
Sheshmani Tiwari. QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices. International Journal of Cheminformatics. 2025; 03(01):50-57.
How to cite this URL:
Sheshmani Tiwari. QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices. International Journal of Cheminformatics. 2025; 03(01):50-57. Available from: https://journals.stmjournals.com/ijci/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 22/05/2025
Accepted 29/05/2025
Published 03/06/2025
Publication Time 12 Days
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