SeeSAR affinity – binding affinity and torsion angle analysis of virtual libraries
This worked example explores ways to assess the binding affinity of docked compounds.
Going from a flat, 2D image of a molecule to a useful 3D model requires accurate generation of low-energy conformational ensembles, factoring in chirality and protonation at physiological pH. Our BioPharmics platform includes the ForceGen methodology for fast, accurate and template-free conformer generation, even on complex macrocycles.
Setting up docking models can be time-consuming, and the results are often unreliable. With Surflex-Dock, you can enhance your workflows and make docking easier with robust, automated docking procedures and easy-to-use workflows, providing top-tier enrichment. This means you’ll find more active compounds when you screen the top-ranked compounds.
With Surflex-Dock, ensembles of protein conformations take into account the variability in the binding pocket and relevant interactions to give a more realistic picture of molecular docking and further enhance the performance of virtual screens.
And to guide the design of more potent compounds, you’ll need accurate non-cognate ligand docking to correctly predict the poses of new ligands for your protein target. The unique combination of eSim and Surflex-Dock in the BioPharmics platform offers industry-leading pose prediction, helping you quickly reach high-affinity compounds.
In some projects, you may have little or no target structural information. 3D ligand-based drug design can overcome these challenges.
Ligand-based methods such as eSim can create a surface representation of the molecule based on shape, electrostatic field, and hydrogen-bond preferences to assess the similarity of new compounds with a known active. eSim offers unparalleled accuracy in virtual screening and pose prediction, helping you find and optimise novel, active compounds more quickly. And, if you have more than one active ligand, eSim can find binding hypotheses that explain their common activity, helping you to understand the SAR for your target and giving even better results for virtual screening.
Predicting the affinity of new compounds is a big challenge. Using the BioPharmics QuanSA (Quantitative Surface-field Analysis) method, you can predict binding affinity with equivalent accuracy to market-leading structure-based methods such as FEP+ and with greater transferability to novel chemistries. This unique, ligand-based approach enables you to build accurate affinity models, even in the absence of protein structure information and with only a small number of known actives, to find novel active compounds.
QuanSA’s multiple-instance machine learning models are physically motivated, accurate, and scaffold-independent. QuanSA also provides prediction confidence metrics, enabling you to confidently predict ligand binding affinity and pose.
Macrocycles hold vast potential against previously “undruggable” targets. But the size and complexity of these molecules means that their design and optimisation is far from easy. To accelerate macrocyclic lead optimisation, you can accurately predict the conformations and properties of macrocycles with the BioPharmics platform.
The BioPharmics platform enables you to apply biophysical constraints to help model very large peptidic macrocycles. You can access different modelling methods including both structure-based and ligand-based machine learning approaches, to support your macrocyclic discovery projects.
Linking 3D structure- and ligand-based design with predictive modelling and SAR analysis gives you a comprehensive picture of the activity and quality of your compounds, to quickly achieve optimal compounds. StarDrop’s SeeSAR modules are fully integrated within its comprehensive environment for small molecule design, optimisation and data analysis. The SeeSAR modules enable easy visualisation of your ligands in their protein environment, assessment of binding affinities, pose generation and fast template docking for interactive 3D design.
Whatever docking platform you prefer, you can run pre-prepared 3D docking and alignment models directly from StarDrop’s SeeSAR module. The Pose Generation Interface (PGI) is compatible with most major docking software platforms, including our Surflex-Dock method and third-party platforms such as FlexX™, Gold™, MOE™, AutoDock Vina™, POSIT™ and more.
This worked example explores ways to assess the binding affinity of docked compounds.
When exploring chemistry space around a known hit or lead, you can use 3D virtual screening to identify new compounds…
From the manuscript “DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design”, Miguel García-Ortegón, Sergio Bacallado, et al¹ have developed…
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