Structure Activity Relationship Analysis and Development

The structure of a chemical implicitly determines its physical and chemical properties and reactivities. Structure-activity relationships (SARs) and quantitative structure–activity relationships (QSARs) are basic and theoretical models to drug research and development, which can be used to predict the physicochemical, biological, and other properties of chemicals, and guide compound optimization. The former one is an (qualitative) association between a chemical substructure and the potential of a chemical. The latter one is a mathematical model that quantitatively relates a quantitative numerical measure of chemical structure, which has been commonly used in the absence of available data for prioritization, classification, and assessment. Classical QSARs methods are commonly used to build linear SAR models for compound series and prioritize analogs.

SARs has long been used to design chemicals with commercially desirable properties with expected pharmacologic and therapeutic activities. Moreover, computer-based modeling methods, as well as AI-driven approaches relating chemical structure to qualitative biological activity and quantitative biological potency have been widely applied in diverse problem settings. CD ComputaBio provides intelligent services of structure activity relationship analysis and development for our clients in different stage of drug discovery, especially for screening large numbers of chemicals and decision making during chemical optimization.


  • nomain-title-log-pic2 Identify the “key” compounds.
  • nomain-title-log-pic2 Propose possible “hole” of the chemical scaffold.
  • nomain-title-log-pic2 Delineate classes of active chemicals representing distinct biological and chemical mechanism domains.
  • nomain-title-log-pic2 Determine the structural features and properties responsible for modulating activity.
  • nomain-title-log-pic2 Modeling approaches that are tailored to issues in drug research and development.

Figure 1 Communication and Modeling Platform for Medicinal Chemists

nomain-drag-pic1Application Scenarios

  • nomain-title-log-pic2 Quick structure clustering
  • nomain-title-log-pic2 Similarity/substructure search
  • nomain-title-log-pic2 Properties predictions
  • nomain-title-log-pic2 Different visualization
  • nomain-title-log-pic2 Model calculation
  • nomain-title-log-pic2 Protein binding site analysis
  • nomain-title-log-pic2 Hit selection
  • nomain-title-log-pic2 Molecule docking and scoring

nomain-drag-pic1Structure Activity Relationship Analysis

A variety of computational methods are available to aid in SAR analysis and compound design. Different statistical and modeling approaches have been introduced to monitor SAR progression of evolving compound series. AI-powered methods are also increasingly applied to model nonlinear SARs and predict novel active compounds.

nomain-drag-pic1SAR Analysis Endpoints

  • Enzymatic assay
  • Cellular assay
  • Selectivity
  • Solubility
  • Permeability
  • H(M/R)LM
  • TDI
  • DDI
  • hERG binding
  • Off target panel

nomain-drag-pic1SAR Analysis Methods

  • R-group decomposition
  • Structure clustering
  • Matched molecular pairs
  • Activity cliffs
  • Similarity matrix
  • Scaffold tree
  • Automatic core scaffold determination
  • Structure activity landscape index
  • Molecular grid maps
  • Principle component analysis
  • SAR visualization
  • Machine learning algorithms
  • Numerical SAR analysis

nomain-drag-pic1SAR Analysis Tools

  • ICM Molsoft (FOCUS)
  • Tibco Spotfire
  • StarDrop
  • Instant Jchem
  • Schrodinger
  • MOE
  • Cresset
  • Data Warrior
  • OSIRIS Property Explorer
  • MultiCASE (MCASE)
  • Various internal developed informatics tools & integrations

nomain-drag-pic1 Structure Activity Relationship Development

The process of developing a SAR is one of attempting to understand and reveal how properties relevant to activity are encoded within and determined by the chemical structure. New SAR modules are constantly being developed. These modules should be tested and refined for use in further validation experiments. Various methodologies have been developed to systematically extract structurally related compound series from data sets of any composition, visualize SAR patterns, and generate virtual candidate compounds. Experts from CD ComputaBio are dedicated to making it possible to comprehensively organize and explore large data sets, visualize SARs, and select candidate compounds for practical applications.


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