Application of Inductive Logic Programming to Structure-Based Drug Design

Developments in physical and biological technology have resulted in a rapid rise in the amount of data available on the 3D structure of protein-ligand complexes. The extraction of knowledge from this data is central to the design of new drugs. We extended the application of Inductive Logic Programming (ILP) in drug design to deal with such structure-based drug design (SBDD) problems. We first expanded the ILP pharmacophore representation to deal with protein active sites. Applying a combination of the ILP algorithm Aleph, and linear regression, we then formed quantitative models that can be interpretated chemically. We applied this approach to two test cases: Glycogen Phosphorylase inhibitors, and HIV protease inhibitors. In both cases we observed a significant (P < 0.05) improvement over both standard approaches, and use of only the ligand. We demonstrate that the theories produced are consistent with the existing chemical literature.

[1]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[2]  Hugo Kubinyi,et al.  3D QSAR in drug design : theory, methods and applications , 2000 .

[3]  D. Joseph-McCarthy.  Computational approaches to structure-based ligand design. , 1999, Pharmacology & therapeutics.

[4]  Thomas Lengauer,et al.  Flexible docking under pharmacophore type constraints , 2002, J. Comput. Aided Mol. Des..

[5]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[6]  R. King,et al.  New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors. , 2002, Journal of medicinal chemistry.

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

[8]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[9]  Michael R. Greenberg,et al.  Chapter 1 – Theory, Methods, and Applications , 1978 .

[10]  A. N. Jain,et al.  Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. , 1994, Journal of medicinal chemistry.

[11]  D. Rognan,et al.  Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. , 2001, Bioorganic & medicinal chemistry letters.

[12]  M J Sternberg,et al.  Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Nada Lavrač,et al.  An Introduction to Inductive Logic Programming , 2001 .

[14]  L. Johnson,et al.  POTENT INHIBITION OF GLYCOGEN PHOSPHORYLASE BY A SPIROHYDANTOIN OF GLUCOPYRANOSE : FIRST PYRANOSE ANALOGUES OF HYDANTOCIDIN , 1995 .

[15]  Ingo Muegge,et al.  Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors , 2000, J. Comput. Aided Mol. Des..

[16]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[17]  M. Pastor,et al.  A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure--activity relationship analysis. , 1997, Journal of medicinal chemistry.

[18]  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.

[19]  Jonathan D. Hirst,et al.  Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines , 1994, J. Comput. Aided Mol. Des..

[20]  K. Jensen,et al.  Specific inhibition of a family 1A dihydroorotate dehydrogenase by benzoate pyrimidine analogues. , 2001, Journal of medicinal chemistry.

[21]  R. Babine,et al.  MOLECULAR RECOGNITION OF PROTEIN-LIGAND COMPLEXES : APPLICATIONS TO DRUG DESIGN , 1997 .

[22]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[23]  H. Kubinyi,et al.  3D QSAR in drug design. , 2002 .