Title:A Linear and Nonlinear QSAR Analysis of Benzimidazole Derivative XY123 in Prostate Cancer Treatment
Volume: 21
Issue: 16
Author(s): Bing Li and Xiaoqiang Liu*
Affiliation:
- Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
Keywords:
Metastatic castration-resistant prostate cancer, benzimidazole derivative XY123, quantitative structure-activity relationship, heuristics, gene expression programming, IC50.
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.
