Computational Chemistry

Computational chemistry at Peakdale Molecular is a highly flexible and innovative service founded on integration with and the requirements of, medicinal chemists and biologists.

We provide a breadth of novel and insightful modelling that can be tailored to individual requirements helping to accelerate projects. Working closely with both chemists and biologists means that we are able to focus on the current needs of a project while anticipating future requirements.

Using both high quality in-house data sets and publicly available data a continual process of model generation, evaluation and refinement is undertaken to improve the quality and reliability of our models.

The goal of computational chemistry at Peakdale is to work closely with all members of a project team to increase understanding of a compounds mechanism of action, and to identify key features of the compound. This information can then be used to focus synthetic effort toward compounds which will improve the project from activity, ADMET, selectivity, and novelty perspectives.

 

KEY CAPABILITIES

 

Structure Based Drug Design

  • Virtual high throughput screening
  • Lead docking using rigorous assessment of protein - ligand interactions
  • QM-MM methods for in-depth understanding of protein - ligand interactions
  • Free energy simulations for improved binding affinity models
  • Molecular dynamics methods to analyze protein conformational changes
  • Induced Fit Docking methods to predict protein changes upon ligand binding

 

Ligand Based Drug Design

  • QSAR based predictive models for ligand optimisation
  • Field analysis methods for binding mode elucidation
  • Scaffold hopping
  • QM calculations for QSAR, ligand conformation determination and energy profiling
  • Pharmacophore screening for rapid compound filtration
  • Field analysis for generation of new lead series

 

Cheminformatics

  • Virtual library design of synthetically tractable compounds
  • Library enumeration and processing using combinations of structure, ligand or ADMET prediction based techniques
  • Property filtration and analysis
  • Similarity and diversity analysis methods
  • Identification of novel IP space

 

Bioinformatics

  • Protein family analysis
  • Homology modelling
  • Active site identification and comparison
  • Off target activity identification and methods of off target activity removal

 

Insilco ADMET Prediction

  • Prediction of key drug-like properties
  • Development of focused and general predictive models
  • Peakradar™ for visualisation of known drug space
  • CNS permeability prediction
  • hERG prediction
  • Key ADMET property predictions