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Volume 21, Issue 16
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

Metastatic Castration-resistant Prostate Cancer (mCRPC) represents a critical challenge in current prostate cancer treatment. Benzimidazole Derivative XY123 has emerged as a novel inhibitor for its treatment.

Objective

This study aims to establish a robust Quantitative Structure-Activity Relationship (QSAR) model for predicting the activity of Benzimidazole Derivative XY123 derivatives, aiding the development of novel anti-prostate cancer drugs.

Methods

Utilizing CODESSA software, descriptors were computed based on various moieties of Benzimidazole Derivative XY123 derivatives. Multiple linear regression models were constructed, and both linear and nonlinear QSAR models were developed using heuristics and gene expression programming.

Results

The linear model with two descriptors demonstrated the best predictive capacity for inhibitor activity, while the nonlinear model generated through Gene Expression Programming (GEP) exhibited correlation coefficients of 0.83 and 0.82 for the training and test sets, respectively. The average errors were 0.03 and 0.05, indicating the stability and the improved predictive ability of the nonlinear model.

Conclusion

The QSAR linear model has an advantage over the nonlinear model in optimizing Benzimidazole Derivative XY123, providing a direction for the development of effective drugs for mCRPC treatment.

© 2024 Bentham Science Publishers
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2024-03-06
2024-12-23

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/content/journals/lddd/10.2174/0115701808291381240226094729
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A Linear and Nonlinear QSAR Analysis of Benzimidazole Derivative XY123 in Prostate Cancer Treatment
Letters in Drug Design & Discovery 21, 3577 (2024); https://doi.org/10.2174/0115701808291381240226094729
/content/journals/lddd/10.2174/0115701808291381240226094729
/content/journals/lddd/10.2174/0115701808291381240226094729
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  • Article Type:     Research Article
Keyword(s): benzimidazole derivative XY123; gene expression programming; heuristics; IC50; Metastatic castration-resistant prostate cancer; quantitative structure-activity relationship

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