QSAR-Co-X

QSAR-Co-X performs multi-target quantitative structure–activity relationship (mt-QSAR) modelling to integrate chemical and biological data into predictive model equations for simultaneous assessment of multiple biological activities or targets.

Key Features:

• Multi-Target Modelling: Enables simultaneous consideration of multiple biological activities or targets within a single model framework.
• Box-Jenkins Moving Average Approach: Employs the Box-Jenkins moving average methodology to develop both linear and non-linear models capturing structure–activity relationships.
• Dataset Management: Provides dataset selection and curation functionalities to ensure relevant, high-quality data are used in model development.
• Descriptor Computation: Computes molecular descriptors to quantify chemical properties for use in predictive QSAR models.
• Comprehensive Results Analysis: Calculates statistical parameters and produces graphical outputs to assess model predictivity and robustness.
• Modular Architecture: Implements Model Development and Screen Predict modules for classification-based modelling.
• Python Implementation: Implemented using Python-based programming.
• Case Studies Validation: Functionality demonstrated through three case studies using previously reported datasets.

Scientific Applications:

• Drug discovery: Predicts compound activities across multiple targets to support lead identification and optimization.
• Toxicology: Assesses compound activity profiles across multiple biological endpoints for toxicological evaluation.
• Environmental chemistry: Investigates structure–activity relationships relevant to environmental chemical assessment.

Methodology:

Computational methods explicitly include dataset selection and curation, computation of molecular descriptors, application of the Box-Jenkins moving average approach to develop linear and non-linear models, calculation of statistical parameters and graphical analysis for model assessment, and classification-based modelling via the Model Development and Screen Predict modules implemented in Python.