Computational chemical biology and cheminformatics

Computational methods are routinely used in drug discovery and lead optimization projects. Within NL-OPENSCREEN ample expertise is available in the application of computational chemical biology (‘big data approaches’) and cheminformatics.

NL-OPENSCREEN offers several services in the domain of computational chemical biology and cheminformatics. More specifically the following services are offered:

  1. QSAR modeling and proteochemometrics (virtual screening). Using chemical & protein descriptors coupled to machine learning (e.g. random forests or deep learning) quantitative structure-activity models (QSAR) can be created for a given target based on NL-OPENSCREEN data or public data. These can subsequently be used to virtually screen NL-OPENSCREEN or vendor chemical databases.
  2. Mode-of- action Elucidation. Using the ChEMBL database and the recently released ExCAPE-DB bioactivity spectrum, models can estimate interaction with the experimentally investigated proteome, or predict toxicity using e.g. Toxcast and Tox21 data.
  3. Cheminformatics. In addition to chemical biology approaches, “classical” cheminformatics techniques are available. For instance: chemical clustering, dimensionality reduction, and visualization techniques.
  4. Focused library design. Virtual libraries can be optimized towards a class of proteins based on data available in the public domain, or be optimized to maximize the content of likely allosteric or orthosteric molecules for a given target family.
  5. Structure based approaches. Given an available crystal structure, methods such as docking or molecular dynamics can be offered to rationalize drug-target interactions and virtually screen for novel hits.