QSAR with CoMFA

Overview

QSAR with CoMFA^® builds statistical and graphical models that relate the properties of molecules (including biological activity) to their structures. These models are then used to predict the properties or activity of novel compounds. Tripos' patented Comparative Molecular Field Analysis (CoMFA) has been used as the method of choice in hundreds of published QSAR studies. A wide variety of structural descriptors can be calculated, including EVA and the molecular fields of CoMSIA™.

Quantitative structure-activity relationships (QSARs) relate a molecule's chemical properties or biological activity to its structure in order to design products with increased effectiveness. QSAR with CoMFA provides tools to build statistical and graphical models of activity from molecular structure, and uses these models to make accurate predictions for the activity of untested compounds. QSAR with CoMFA organizes structures and their associated data into Molecular Spreadsheets™, calculates molecular descriptors, and performs sophisticated statistical analyses that reveal patterns in structure-activity data. QSAR with CoMFA is fully integrated with SYBYL^® to enable visualization and analysis of structure-activity relationships.

QSAR with CoMFA Brochure (748k)

Contour plots from a CoMSIA analysis of thrombin inhibitors. (Left) Regions of favorable steric interactions are shown in green; sterically unfavorable regions are shown in yellow. (Right) Blue contours indicate regions where hydrophobic interactions enhance binding; red contours show regions where hydrophobic properties decrease affinity. These contours were used to design a novel inhibitor, displayed in blue, predicted to have ~100x greater affinity. The piperidine ring of the original inhibitor was enlarged to a decaline system in order to occupy regions that favor both steric bulk and hydrophobic groups. The methyl ester was changed to a methyl group to reduce unfavorable steric interactions while still occupying a region favorable for hydrophobic interactions.

Key Benefits

• Develop quantitative structure-activity relationships
• Predict the properties and activities of untested molecules
• Compare different QSAR models statistically and visually
• Optimize the properties of a lead compound
• Validate models of receptor binding sites
• Generate hypotheses about the characteristics of a receptor binding site
• Prioritize compounds for synthesis or screening
• Determine key structural requirements for high affinity receptor ligands