Progress with modeling activity landscapes in drug discovery.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Machine Learning

Background:

• Activity landscapes (ALs) represent compound data with target-specific activity.
• ALs characterize structure-activity relationships (SARs) for specific targets.
• The popularity of AL modeling has increased with accessible compound data sets.

Purpose of the Study:

• Introduce concepts and basis of AL modeling.
• Discuss different AL modeling approaches and recent developments.
• Highlight the application of mathematical, statistical, and machine learning tools in ALs.

Main Methods:

• Review of AL modeling approaches: compound-pair based, dimensionality reduction, and network approaches.
• Discussion of mathematical, statistical, and machine learning tools for AL modeling.
• Detailed coverage of AL modeling using chemical space networks.

Main Results:

• AL modeling relies on molecular representations and similarity metrics.
• Recent developments focus on advanced mathematical and machine learning techniques.
• Chemical space networks offer a detailed approach to AL modeling.

Conclusions:

• AL modeling has evolved from a descriptive approach to a more sophisticated analytical tool.
• The integration of statistics, machine learning, and network theory enhances AL designs.
• Optimizing molecular representations is key to developing predictive AL tools.