CombiGlide
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CombiGlide: Combinatorial technology and core hopping for lead discovery and optimization

CombiGlide combines accurate ligand-receptor scoring, clever combinatorial docking algorithms, and highly efficient core-hopping technology to design focused libraries and identify new scaffolds. These technologies greatly facilitate lead discovery and optimization efforts.

The Advantages of Computational Lead Optimization

The virtual chemical space that chemists are interested in is too large to be synthesized and screened, even using modern methods of combinatorial chemistry and robotic synthesis. Therefore, there is a real need for efficient and reliable methods to rationally select the optimal library members for synthesis. Additionally, once a promising lead compound is discovered, different core scaffolds as well as side-chain substitutions must be enumerated and examined to evaluate relative binding affinities towards a particular target. Accurate ligand-receptor scoring coupled with intelligent and efficient combinatorial docking and core-hopping methods can accelerate lead optimization and aid in designing the optimal, focused compound library for further synthesis.

Schrödinger's CombiGlide can flexibly vary the core or side-chain substitutions, creating virtual combinatorial libraries that may be screened for leads, identify novel scaffolds, or generate focused libraries in support of lead optimization efforts.

Features

Library enrichment:
CombiGlide identifies the most effective reagent combinations to produce focused libraries that have the highest likelihood of binding tightly to the target protein. CombiGlide dramatically reduces the overwhelming combinatorial space down to manageable library sizes by selecting and ranking reagents.

Core hopping:
CombiGlide performs core hopping beginning with a lead compound/receptor complex and searches among candidate core structures to identify alternate cores exhibiting similar binding modes as the lead that can accommodate the side chains in their optimal orientation.

Idea generation:
CombiGlide analyzes the chemical features of the virtual library in terms of their pharmacophore properties to generate insights into the chemical functionalities the receptor prefers at each core position, which can be used to guide the selection of reagents in further experimental and computational explorations.

ADME properties:
CombiGlide can optionally filter compounds using predicted ADME properties, eliminating from consideration compounds that may bind well but exhibit undesirable pharmacokinetic profiles.

Easy-to-use interface:
CombiGlide guides a user through the chosen workflow with an intuitive step-by-step interface. The Maestro interface provides helpful structural visualization and analysis tools.