Use of Electron Density Critical Points as Chemical Function-Based Reduced Representations of Pharmacological Ligands

In this paper, we propose a reduced representation of molecules of pharmacological interest based on their chemical functions. The proposed representations of the molecules are obtained by a topological analysis of their electron density maps at medium resolution, leading to graphs of critical points. The distribution of the different types of critical points are compared at various levels of resolution for a training set of 22 molecules in order to define the optimal resolution level leading to the best representation of the various chemical functions. The reduced representations can in the future be used for molecular similarity research and pharmacophore proposals.

[1]  Igor Jurisica,et al.  Improving Objectivity and Scalability in Protein Crystallization: Integrating Image Analysis With Knowledge Discovery , 2001, IEEE Intell. Syst..

[2]  P Willett,et al.  Searching for pharmacophoric patterns in databases of three‐dimensional chemical structures , 1995, Journal of molecular recognition : JMR.

[3]  S Fortier,et al.  Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure. , 1993, Acta crystallographica. Section D, Biological crystallography.

[4]  Daniel P. Vercauteren,et al.  Critical Point Analysis of Calculated Electron Density Maps at Medium Resolution: Application to Shape Analysis of Zeolite-Like Systems , 1997 .

[5]  F. Allen The Cambridge Structural Database: a quarter of a million crystal structures and rising. , 2002, Acta crystallographica. Section B, Structural science.

[6]  Daniel P. Vercauteren,et al.  Topological analysis of electron density maps of chiral cyclodextrin-guest complexes: a steric interaction evaluation , 1995 .

[7]  S Fortier,et al.  Molecular scene analysis: application of a topological approach to the automated interpretation of protein electron-density maps. , 1994, Acta crystallographica. Section D, Biological crystallography.

[8]  Richard A. Lewis,et al.  Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Ashwin Srinivasan,et al.  Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL , 1998, Machine Learning.

[10]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[11]  N Meurice,et al.  Comparison of benzodiazepine-like compounds using topological analysis and genetic algorithms. , 1998, SAR and QSAR in environmental research.

[12]  Holger Wallmeier,et al.  Computer‐Assisted Molecular Design (CAMD)—An Overview , 1987 .

[13]  Christian Lemmen,et al.  Computational methods for the structural alignment of molecules , 2000, J. Comput. Aided Mol. Des..

[14]  Robert P. Sheridan,et al.  The Most Common Chemical Replacements in Drug-Like Compounds , 2002, J. Chem. Inf. Comput. Sci..