Progress with modeling activity landscapes in drug discovery

ABSTRACT

Introduction: Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity.

Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail.

Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

KEYWORDS:

• Activity cliffs
• activity landscapes
• chemical space networks
• compound data sets
• molecular similarity
• networks
• structure-activity relationships

Article highlights

• Activity landscapes are defined as representations integrating structural similarity and activity relationships.

• Activity landscapes crucially depend on the representation of chemical space, i.e., how structural similarity is assessed.

• Activity landscapes are characterized by continuous and discontinuous regions and activity cliffs are the most prominent features of these landscapes.

• Current activity landscape designs are either compound pair-based, based on dimensionality reduction, or utilize networks.

• Mathematical and statistical tools aid in the assessment of significance of observed SAR features.

• Networks are an extremely flexible approach to activity landscape modeling.

• Activity landscape modeling remains a mainly descriptive tool, however progress regarding predictive applications is being made.

• Designing optimized representations that efficiently encode activity-relevant features of molecules is a goal for future research.

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Declaration of interest

The author is an employee of the University of Bonn and has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.