Prioritize the best molecules in drug discovery using Spark™ and Flare™ FEP
Free Energy Perturbation (FEP) methods enjoy a strong reputation for delivering reliable and robust binding affinity predictions for small molecule ...
Robust QSAR models enable computational and medicinal chemists to accurately predict the biological activity and ADMET properties of new compounds. This saves time and resources in molecule design projects, by allowing research chemists to prioritize the best molecules to make.
Cresset’s comprehensive molecule design platform, Flare, enables you to choose from a number of different quantitative models for both regression and classification analysis.
Field QSAR uses Cresset 3D descriptors to provide a global view of your SAR data. This method is very efficient at modeling specific ligand-protein interactions to predict biological activity at the target of interest. Visual feedback helps you identify the common favorable and unfavorable features within your molecules so that you can further improve your design.
Machine Learning models are also available, which work using both 2D and 3D descriptors. Whole molecule 2D descriptors are particularly useful when investigating ADMET properties not involving a direct ligand-protein interaction.
In our webinar ‘Building predictive QSAR models with Flare™ to prioritize the best molecules’, we demonstrate how to:
Request a recording of ‘Building predictive QSAR models with Flare to prioritize the best molecules’ or request an evaluation to try Flare on your project.