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Optima: a GUI-based toolkit for developing and validating interpretable machine learning based supervised classification QSAR models
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DOI：10.1080/1062936x.2026.2649822.png)
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摘要
En 中文
The rapid advancement of ML algorithms and the increasing availability of large datasets have significantly transformed the landscape of predictive modelling in scientific research. In this context, we introduce Optima (OPTimized Interpretable Model Building & Analysis Toolkit), a user-friendly, Python-based GUI designed to simplify and accelerate the development of interpretable ML-based classification QSAR/QSPR/QSTR models. This toolkit offers an intuitive graphical user interface (GUI), enabling users with domain knowledge but limited coding experience to efficiently optimize, construct, and interpret various ML-based classification models. A most highlighting feature of this GUI is its fully customizable settings panel, allowing users to modify colour schemes, font sizes, axis labels, and plot dimensions to suit publication or presentation needs. By combining robust optimization with an explainable approach, the Optima toolkit improves classification QSAR model performance while ensuring transparency and reproducibility. This platform addresses a critical need by providing an intuitive GUI for rational dataset splitting, efficient feature selection, and the development of seven different ML-based classification QSAR models, covering the entire workflow from optimization to interpretation. The toolkit is presently available for Windows and can be downloaded from the provided link (https://github.com/Rahul-Roy-21/OPTIMA).
Keyword:
Machine learning (ML)
classification QSAR
optimization
interpretation
reproducibility
期刊
S
IF:
2.4
论文数:
46
被引数:
1.4K
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引用论文
Srivastava, M.; Nandan, S.; Zaidi, A.; Samani, A.S.; Shukla, V.; Aslam, H.; Srivastava, A.; Maurya, P.; Khan, M.A.; Khan, M.F. Artificial Intelligence Driven Applications in Analytical Chemistry, Drug Discovery, and Food Science: Advancements, Outlook, and Challenges. ChemistrySelect 2025, 10, e202404446. [Google Scholar] [CrossRef]Srivastava, M.; Nandan, S.; Zaidi, A.; Samani, A.S.; Shukla, V.; Aslam, H.; Srivastava, A.; Maurya, P.; Khan, M.A.; Khan, M.F. 人工智能驱动的分析化学、药物发现和食品科学中的应用：进展、展望与挑战。ChemistrySelect 2025, 10, e202404446. [Google Scholar] [CrossRef].png)
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