SYBYL-X Suite: SAR and QSAR for Lead Optimization

A key challenge faced by discovery scientists during Lead Optimization (LO) is selecting which compounds to make from a large number of potential synthesis candidates. In LO, a difference of a factor of ten in the potency of a drug candidate can make the difference between a successful candidate and project failure. The challenge is made more difficult because the analogs are often quite similar, differing by just one or two R-groups.

QSAR methods allow researchers to go beyond merely categorizing structures as active or inactive. QSAR helps them to predict the level of biological activity or potency for a set of close analogs to prioritize ideas in Lead Optimization.

Certara developed these QSAR techniques to advance Lead Optimization:

  • Comparative Molecular Field Analysis (CoMFA) was the first 3D QSAR method, and is an industry standard with thousands of publications demonstrating CoMFA’s utility for molecular discovery. With CoMFA, researchers build statistical and graphical models that relate the chemical and biological properties of molecules to their 3D structures and the 3D steric and electrostatic properties. These models are then used to predict the properties or activity of novel compounds.
  • Hologram QSAR (HQSAR) is a novel 2D QSAR method, and uses counts of key molecular substructures and PLS to generate fragment-based structure-activity relationships. Validation studies have shown that HQSAR has predictive capabilities comparable to those of much more complicated 3D-QSAR techniques.
  • Topomer CoMFA minimizes the preparation needed for 3D QSAR analysis. These models can be created in minutes, and can easily be used by both QSAR experts and QSAR non-experts. Because pose generation is automated with Topomer CoMFA, researchers can:
    • Easily generate models for multiple biological endpoints
    • Identify novel ideas for R-groups that are most likely to lead to improvements in activity using virtual screening based on predictions from 3D QSAR models
    • Generate 100’s to 1000’s of predictive QSAR models for chemogenomic studies automatically by mining large databases of chemical and biological data

The workflow oriented QSAR Project Manager streamlines the organization of QSAR datasets, QSAR models, and QSAR predictions. With the QSAR project manager, scientists can work more efficiently and effectively, and it is much easier for a non-expert modeler to generate QSAR results.

Additionally, Certara’s QSAR science is accessible via Python, a popular scripting environment that allows researchers to build custom workflows and deploy QSAR predictions out to the wider discovery team.

Learn more about the SYBYL-X Suite:

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