SYBYL®-X Suite

SYBYL-X enables researchers to understand and balance the competing SAR’s for each of the multiple criteria a successful drug candidate must meet. 

Visualize and explore relationships between multiple properties with the analysis tools in the new Molecular Data Explorer (MDE) in SYBYL-X, and obtain insights into your project data in minutes.  For example: 

• You can quickly compute cLogP for a set of compounds of interest, plot a histogram of cLogP and a histogram of activity in your cellular assay, and by selecting the structures with cLogP greater than 3, see how the structures with high predicted logP are distributed in terms of their cellular activity. 
• Or, using a structure similarity map, you can get a visual structural clustering that shows activity/selectivity islands and cliffs at a glance.  The MDE allows scientists to merge and combine chemical structure data and biological data from multiple sources so that all of the data can be analyzed together, and to explore the relationships between the multiple computed or measured properties.

Predictive Models for Multiple Properties

Predictive models that cover all of the parameters relevant to successful clinical outcome are needed to design drugs which balance multiple criteria efficiently.  Recent advances in SYBYL-X’s 3D QSAR capabilities make modeling multiple biological endpoints quick and easy.  

Topomer CoMFA, SYBYL-X’s latest QSAR method, enables researchers to create 3D QSAR models in minutes instead of weeks, and to automatically generate 100’s to 1000’s of predictive QSAR models for chemogenomic studies by mining large databases of chemical and biological data.

Nearly half of drug candidates fail due to lack of adequate safety in pre-clinical testing.   Predictive methods that allow researchers to identify safety and/or off-target pharmacology much earlier in the drug discovery and development process are now available in SYBYL-X and allow better decision making so that chemistry efforts can be focused on the areas with the best chance of success in pre-clinical and clinical safety; biological/Safety evaluation explores the areas of greatest risk, and new therapeutic application can be found for a failed development candidate in order to rescue lost investment.  Some examples include:

• SYBYL-X's Topomer Search technology was used by scientists at a major pharmaceutical company to successfully predict hERG liability, a key anti-target which can lead to a potentially fatal disorder called long QT syndrome.  
• Topomer CoMFA’s QSAR methods have been successfully applied to model cytochrome P450 activity and successfully guide lead optimization teams to compounds with improved metabolic profiles.  
• Surflex-Sim’s 3D molecular similarity has been shown to reveal non-trivial off target biological relationships that would not be found on the basis of simplistic 2D chemical descriptions. 
• Predictive QSAR models for 100’s of biological targets have been developed using Topomer CoMFA methods to mine, in an automated fashion, the large public repositories of chemical and biological data that are now available.  These predictive models provide both accurate biological predictions and a chemical rationale for understanding the SAR at each receptor. 

SYBYL-X allows researchers to perform critical lead discovery tasks such as hit or lead expansion and lead or scaffold hopping, and to consider critical molecular properties or predicted ADME and physical properties early in the discovery process.

• Key ligand-based design tasks, like structure-activity relationship modeling, pharmacophore hypothesis generation, molecular alignment, and ADME prediction, are addressed effectively and efficiently in SYBYL-X.
• When a protein structure is available, SYBYL-X’s structure-based virtual screening capabilities allow researchers working in lead identification to identify promising lead candidates that interact with a receptor of interest from databases of in-house or commercially available compounds.

Additionally, chemical library design techniques allow researchers to develop combinatorial or focused compound collections useful in lead identification. Truly diverse, representative, and synthetically-feasible compound sets speed the identification of active small molecules, and SYBYL-X addresses critical library design tasks, such as library creation and molecular diversity enhancement.

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

Certara continues our history of thought leadership in the area of QSAR, which allows researchers to go beyond categorizing structures as active or inactive, to make accurate predictions the level of biological activity or potency for a set of close analogs, which is vital to effectively prioritize ideas in Lead Optimization.  Accurate 3D QSAR models allow researchers to prioritize their ideas and select those most likely to advance project objectives. 

Tripos (now Certara) was an early pioneer in QSAR and in particular 3D QSAR and we continue to build on that tradition with scientific innovation and solutions for Lead Optimization. 

• We introduced CoMFA, the first 3D QSAR method and now an industry standard with literally thousands of literature publications demonstrating CoMFA’s utility for molecular discovery. 
• We introduced HQSAR, a novel 2D QSAR method based on molecular holograms.  
• With Topomer CoMFA, we are revolutionizing 3D QSAR all over again.  Because pose generation is automated with Topomer CoMFA, researchers can:
  • Easily generate models for multiple activities
  • 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