Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Quantitative Structure-Activity Relationship (QSAR) studies are crucial for drug discovery.
• Sirtuin 1 (SIRT1) activators are of therapeutic interest for various diseases.
• Developing robust QSAR models requires rigorous validation and applicability domain assessment.

Purpose of the Study:

• To develop a consensus QSAR model for 65 SIRT1 activators using Hierarchical QSAR technology (HiT QSAR).
• To identify key molecular fragments responsible for modulating SIRT1 activation.
• To ensure the developed models adhere to OECD principles for reliable predictions.

Main Methods:

• Utilized Hierarchical QSAR technology (HiT QSAR) for consensus modeling.
• Employed Simplex Representation of Molecular Structure (SiRMS) for descriptor generation.
• Developed predictive models using Partial Least Squares (PLS) and validated through Y-scrambling and domain of applicability assessments.

Main Results:

• A consensus QSAR model with high predictive power (r² = 0.830, Q² = 0.754) was successfully established.
• Evaluated prediction quality using leverage, ellipsoid, and local tree domain of applicability methods.
• Mechanistic interpretation identified specific molecular fragments that enhance or reduce SIRT1 activation.

Conclusions:

• The developed consensus QSAR model provides a reliable tool for predicting SIRT1 activator properties.
• The identified molecular fragments offer insights for the rational design of novel SIRT1 modulators.
• This study demonstrates the utility of HiT QSAR in advancing the understanding and development of SIRT1-targeting agents.