Prediction of telomerase inhibitory activity for acridinic derivatives based on chemical structure

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Cancer Biology

Background:

• Telomerase is a key enzyme in over 85% of cancers, driving uncontrolled cell proliferation.
• Inhibiting telomerase is a promising anticancer strategy, with G-quartet stabilization being a focus.
• Quantitative Structure-Activity Relationship (QSAR) studies for predicting telomerase inhibition by acridinic derivatives are lacking.

Purpose of the Study:

• To develop a QSAR classification model for predicting the telomerase inhibitory activity of acridinic derivatives.
• To identify molecular descriptors and structural features associated with potent telomerase inhibition.
• To provide a tool for the rational design of novel anticancer agents targeting telomerase.

Main Methods:

• A linear discriminant model was employed for classification.
• The model utilized molecular descriptors, including structural fragments and group information.
• A dataset of 90 acridinic derivatives, categorized by inhibitory concentration (IC50), was used for model training and validation.

Main Results:

• The final classification model achieved a high accuracy of 85.33% (87.50% sensitivity, 82.85% specificity).
• Cross-validation and external prediction sets demonstrated strong predictive ability (up to 90% accuracy).
• The model successfully predicted the inhibitory concentration for novel acridine compounds.

Conclusions:

• The developed QSAR model possesses significant predictive power for telomerase inhibition in acridinic derivatives.
• This model can be prospectively applied in the molecular design and mechanism of action studies of anticancer compounds.
• The findings facilitate the discovery of new acridinic compounds with potential anticancer properties targeting telomerase.