Harness cutting edge models for 3D ligand-based and structure-based drug design

Struggling to generate accurate conformers for your macrocycles or structurally-novel compounds? With ForceGen, you can identify the most likely conformers for all your compounds, from small molecules to large macrocycles.

ForceGen’s patented, template-free method systematically applies 3D physical manipulations to explore the geometrical configurations of your ligands, generating ensembles of low-energy conformers. It can evaluate complex macrocycles in minutes, with the option of applying NMR constraints to increase search efficiency.

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Access top-tier docking enrichment and accurate pose prediction with reliable automated docking procedures. Automate protein preparation and reduce inappropriate bias with Surflex™-Dock’s easy-to-use workflows. Surflex™-Dock supports your 3D structure-based design efforts, allowing you to model ligands from small molecules to large macrocycles.

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.

Surflex™-Dock enables highly accurate non-cognate ligand docking, so you can correctly predict poses for future potential ligands for your protein complex, making use of known prior bound ligand poses with eSim.

Surflex™-Dock predictions can be accessed through our intuitive and highly visual PyMOL GUI.

Looking for fast, reliable 3D ligand-based drug design and virtual screening? eSim considers molecular similarity from a protein’s perspective of a ligand, creating a surface representation of the molecule based on shape, electrostatic field, and hydrogen-bond preferences. This information can then be used to:

• Carry out large scale, high performance 3D virtual screening against known actives to find novel potent compounds
• Achieve significantly higher enrichment in your virtual screens than leading competitors
• Inform 3D design with industry-leading pose prediction

eSim is also available as part of our StarDrop drug discovery platform, so you can include 3D ligand-based drug design as part of your integrative, intuitive drug discovery workflow.

The BioPharmics QuanSA (Quantitative Surface-field Analysis) method lets you predict the affinity of your ligands using machine learning models based on physical principles. QuanSA’s 3D ligand-based models achieve equivalent accuracy to market-leading methods such as FEP+, while providing transferablility, even across scaffolds. With QuanSA, you can:

• Predict binding affinity without protein structural information
• Visualise the key interactions that drive molecular affinity through our QuanSA plugin for PyMOL, to guide the design of more potent compounds 
• Build accurate affinity models, even with only a small number of known actives
• Find novel active compounds with prediction confidence metrics