An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands

J Chem Inf Model. 2006 Nov-Dec;46(6):2610-22. doi: 10.1021/ci600334u.


A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • Binding Sites
  • Chemistry, Pharmaceutical / methods*
  • Combinatorial Chemistry Techniques*
  • Cytochrome P-450 Enzyme System / chemistry*
  • Drug Evaluation, Preclinical / methods*
  • Drug Industry
  • Humans
  • Ligands*
  • Models, Chemical
  • Models, Statistical
  • Polymorphism, Genetic
  • Principal Component Analysis
  • Proteins / chemistry*
  • Quantitative Structure-Activity Relationship
  • Technology, Pharmaceutical / methods*


  • Ligands
  • Proteins
  • Cytochrome P-450 Enzyme System