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Computational chemistry techniques provide additional information and exploration opportunities for modern drug discovery efforts. Our team fully integrates these techniques into our workflows, so that they can bring added value to our compound design and synthetic efforts. A full range of ligand- and structure-based approaches are available, either to support projects or as independent in-silico services.  We use a variety of industry standard software (e.g. Schrodinger suite) combined with open source and in-house customizations.  Medicinal chemistry results integrate seamlessly with our chemistry teams and with our web-based project browsing and structure viewing software to streamline our design and results analysis.


  • Powerful and efficient algorithms for analysis of large compound libraries
  • Molecular property calculations and integration with chemistry browsers
  • Tailored models for physicochemical descriptors and ADMET parameter predictions
  • Ready to use databases of pre-analysed viable commercial compounds
  • Reagent selection for chemistry libraries / generation of virtual libraries

Structure-Based Drug Discovery

  • Automated cross-docking procedures, integrated with chemistry workflows
  • Molecular dynamics simulations: enhanced conformational sampling, free energy calculations
  • Free energy perturbation calculations
  • Protein-protein interaction models
  • De-novo drug design

Ligand-Based Approaches

  • Broad implementation of QSAR and machine learning models (multiple approaches from random forest to neural networks)
  • Field-based approaches to identify binding contributions
  • Pharmacophore models and novel scaffold design
  • Similarity searching across large chemical spaces
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