CompChem Solutions
Discovery & Development Consulting Services
People
Dr S. Boyd

Dr Susan Boyd

Susan has worked in the computational chemistry or chemoinformatics departments of Pfizer (Sandwich), Celltech (Cambridge) & Scynexis (Ongar, Essex). Prior to that she worked with Molecular Simulations Inc (now Accelrys) as Product Specialist for their library design tools.

An organic chemist by training, she has a wealth of experience in the application of chemoinformatics & molecular design tools to accelerate the drug discovery process, both for affinity prediction and for identification of potential ADMET issues.

Her experience spans:

  • Protein structure-based design.
  • Pharmacophore modelling.
  • Statistical data modelling (QSAR, neural nets, decision trees).
  • Library enumeration & design.
  • Chemoinformatics/Data handling
  • Virtual screening (2D & 3D).
  • Training of chemists and computational chemists.

Dr Dorica Naylor

Dorica is an independant computational chemistry consultant who can assist with CompChem Solutions projects when appropriate. Dorica has worked in the Computational Chemistry and Cheminformatics departments of Parke Davis/Pfizer (Cambridge), Peptide Therapeutics (Cambridge), Millennium (Cambridge) & Celltech/UCB (Cambridge).

Initially she worked as a physical chemist in the Rudjer Boskovic Institute (Zagreb), then as a computational chemist in Prof. Garland Marshall’s Lab in Washington University School of Medicine (St. Louis, USA), and then in Prof. Janet Thornton’s Lab in UCL (London).

A pharmacist by training, with a PhD in Physical Chemistry, Dorica has a wealth of experience in the application of Molecular Design methods and techniques and Cheminformatics tools to accelerate the drug discovery process, both for affinity/selectivity predictions and for identification of potential ADMET issues.

Dr D. Naylor

Her experience spans:

  • Structure-based drug design.
  • Homology modelling.
  • Pharmacophore modelling.
  • 2D/3D virtual screening.
  • Statistical data modelling (2D/3D QSAR, neural nets, RP).
  • Data handling.
  • ADMET predictions.
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