Pharmacophore Features Distributions in Different Classes of Compounds

A pharmacophore analysis approach was used to investigate and compare different classes of compounds relevant to the drug discovery process (specifically, drug molecules, compounds in high throughput screening libraries, combinatorial chemistry building blocks and nondrug molecules). The distributions for a set of pharmacophore features including hydrogen bond acceptors, hydrogen bond donors, negatively ionizable centers, positively ionizable centers and hydrophobic points, were generated and examined. Significant differences were observed between the pharmacophore profiles obtained for the drug molecules and those obtained for the high-throughput screening compounds, which appear to be closely related to the nondrug pharmacophore distribution. It is suggested that the analysis of pharmacophore profiles could be used as an additional tool for the property-based optimization of compound selection and library design processes, thus improving the odds of success in lead discovery projects.

[1]  H. van de Waterbeemd,et al.  Property-based design: optimization of drug absorption and pharmacokinetics. , 2001, Journal of medicinal chemistry.

[2]  A. Davis,et al.  Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis. , 1999, Angewandte Chemie.

[3]  Andrew R. Leach,et al.  Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery , 2001, J. Chem. Inf. Comput. Sci..

[4]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[5]  J. Drews Drug discovery: a historical perspective. , 2000, Science.

[6]  Tudor I. Oprea,et al.  Is There a Difference between Leads and Drugs? A Historical Perspective , 2001, J. Chem. Inf. Comput. Sci..

[7]  Q. Al-Awqati One hundred years of membrane permeability: does Overton still rule? , 1999, Nature Cell Biology.

[8]  Tudor I. Oprea,et al.  Property distribution of drug-related chemical databases* , 2000, J. Comput. Aided Mol. Des..

[9]  Robert D Clark,et al.  Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. , 1996, Journal of medicinal chemistry.

[10]  Ajay,et al.  Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? , 1998, Journal of medicinal chemistry.

[11]  John M. Barnard,et al.  Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..

[12]  Mark A. Murcko,et al.  Virtual screening : an overview , 1998 .

[13]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[14]  John Bradshaw,et al.  Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms , 1998, J. Chem. Inf. Comput. Sci..

[15]  Miklos Feher,et al.  Property Distributions: Differences between Drugs, Natural Products, and Molecules from Combinatorial Chemistry , 2003, J. Chem. Inf. Comput. Sci..

[16]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[17]  Leach,et al.  The in silico world of virtual libraries. , 2000, Drug discovery today.

[18]  Michael H. Abraham,et al.  Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design , 1989 .

[19]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[20]  G. Bemis,et al.  Properties of known drugs. 2. Side chains. , 1999, Journal of medicinal chemistry.

[21]  I. Muegge,et al.  Simple selection criteria for drug-like chemical matter. , 2001, Journal of medicinal chemistry.