Applications

Science | Better Molecular Description | Alignment | Similarity | Machine Learning | Applications

Our hydrophobic molecular descriptors provide a unique representation of chemical compounds. This description allows to compare and find molecules with similar molecular properties, even if they don’t share the same molecular structure or atomic distribution. The application of these descriptors for molecular alignment and comparison permits to study a larger and more complex chemical space in order to find new chemical entities. As a consequence, our descriptors have different applications:

  • for High-Throughput Virtual Screening (HTVS) campaigns: Using a single molecule as a reference compound, commercial or public libraries can be explored using our hydrophobic descriptors to find new chemical entities or identify new chemical scaffolds. 
  • for backup compounds: The identification of new molecular scaffolds and families permits to increase the chances to find a lead compound and to find molecules not protected by existing IP.
  • for High-Throughput Virtual Screening (HTVS) campaigns: Using a single molecule as a reference compound, commercial or public libraries can be explored using our hydrophobic descriptors to find new chemical entities or identify new chemical scaffolds. 
  • for backup compounds: The identification of new molecular scaffolds and families permits to increase the chances to find a lead compound and to find molecules not protected by existing IP.
  • for molecular properties prediction: Thanks to accurate 3D field-based alignment and similarity score, the descriptors permit to generate precise QSPR models, predicting different molecular properties associated to the 3D structure of the molecules.
  • for Lead Optimization (LO): The generation of accurate QSPR models with hydrophobic descriptors can be applied to the optimization of your lead compounds, improving their PK/PD, ADME/Tox, and other physicochemical properties.
  • for pharmacophore generation: Hydrophobic descriptors are compatible with pharmacophoric models. In contrast to other models (constrained to spatial positions), field-based pharmacophore permits to find new chemical scaffolds thanks to continuum description of the molecule’s properties.
  • to improve structure-based methods: Structure-based drug design methods, like molecular docking, can take advantage of our hydrophobic descriptors, improving the hit rate identification. When combining docking poses with our field-based scoring function, the number of retrieved hits is superior than structure-based or ligand-based methods applied independently.
  • to define the Mechanism Of Action (MOA): Compounds with different molecular scaffold can share the same MOA. This is because different molecular entities with different molecular shape can share similar hydrophobic profiles, sharing the binding mode with respect to the same receptor.