Summary
A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier–Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound’s binding affinity. This scheme has relatively poor predictivity based both on Kier–Hall valence connectivity indices similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier–Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.
Similar content being viewed by others
References
Krueger T.E., Buenger P., 1965 The role of the therapeutic regimen in dosage design Chemotherapia 10: 61 129
Peters T., All about Albumin: Biochemistry, Genetics, and Medical Applications, Academic Press, San Diego. 1995
Goodman G.A., The Pharmacological Basis of Therapeutics. 9. McGraw-Hill, New York: 1996
Holford, N.H.G. and Benet, L.Z., In Katzung, B.G. (Ed.), Basic and Clinical Pharmacology, 7th Edition, Appleton and Lange, Stamford, CT, 1998, pp. 34–49
Torres P.J., 1996 All about Albumin: Biochemistry Genetics and Medical Applications. Academic Press, San Diego
Sudlow G., Birkett D.J., Wade D.N., 1975 The characterization of two specific drug binding sites on human serum albumin Mol. Pharm. 11(6):824
Sudlow G., Birkett D.J., Wade D.N., 1976 Further characterization of specific drug binding sites on human serum albumin Mol. Pharm. 12(6):1052
Watanbe S., Tanase K.N., Maruyama T., Kragh-Hansen U., Otagiri M., 2000 Role of Arg-410 and Tyr-411 in human serum albumin for ligand binding and esterase-like activity Biochem. J. 349:813
Bhattacharya A.A., Curry S., Franks N.P., 2000 Binding of the general anesthetics propofol and halothane to human serum albumin: high-resolution crystal structures J. Biol. Chem. 275:38731
Petitpas I., Bhattacharya A.A., Twine S., East M., Curry S., 2001 Crystal structure analysis of warfarin binding to human serum albumin anatomy of drug site I J. Biol. Chem. 276:22804
Morris J.J., Bruneau P.P., 2000 Prediction of physicochemical properties. In: Böhm H.-J., Schneider G., eds. Virtual Screening for Bioactive Molecules. Weinheim: Wiley–VCH Verlag GmbH, pp. 33–56
Kratochwil N.A., Huber W., Müller F., Kansy M., Gerber P.R., 2000 Predicting plasma protein binding of drugs: a new approach Biochem. Pharmacol. 64(9):1355
Lesk A.M., 1998 Extraction of geometrically similar substructures: least-squares and Chebyshev fitting and the difference distance matrix Proteins: Struct., Funct., Genet. 33(3):320
Kubinyi H., Hamprecht F.A., Mietzner T., 1998 Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices J. Med. Chem. 41(14):2553
So S.S., Karplus M., 1997 Three-dimensional quantitative structure–activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations J. Med. Chem. 40(26):4347
So S.S., Karplus M., 1997 Three-dimensional quantitative structure–activity relationships from molecular similarity matrixes and genetic neural networks. 2. Applications. J. Med. Chem. 40(26):4360
Good A.C., So S.S., Richards W.G., 1993 Structure–activity relationships from molecular similarity matrices J. Med. Chem. 36(4):433
Cruz R., Lopez N., Quintero M., Rojas G., 1997 Cluster analysis from molecular similarity matrixes using a non-linear neural network J. Math. Chem. 20(3, 4):385
Kubinyi H., A general view on similarity and QSAR studies. In van de Waterbeemd H., Testa B., Folkers G., Eds.; Computer-Assisted Lead Finding and Optimization: Current Tools for Medicinal Chemistry; VHCA: Basel, and Wiley–VCH: Weinheim, 1997; pp. 7–28
Duca J.S., Hopfinger A.J., 2001 Estimation of molecular similarity based on 4D-QSAR analysis: formalism and validation J. Chem. Inf. Comput. Sci. 41:1367
Dollery C., Therapeutic Drugs. 2. Churchill Livingstone, Edinburgh: 1999
Kier L.B., Hall L.H., 1981 Derivation and significance of valence molecular connectivity J. Pharm. Sci. 70:583
SAS, Version 8.1 for Windows, SAS Institute Inc., 2001
Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J. 1997 Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 23(1–3):3
Seri-Levy A., Richards W.G., 1993 Chiral drug potency: Pfeiffer’s rule and computed chirality coefficients Tetrahedron Asymmetry 4:1917
Seri-Levy A., West S., Richards W.G., 1994 Molecular similarity, quantitative chirality and QSAR for chiral drugs J. Med. Chem. 37:1727
Burt C., Huxley P., Richard W.G., 1990 The application of molecular similarity calculations J. Comp. Chem. 11:1139
Hall L.M., Hall L.H., Kier L.B., 2003 QSAR modeling of β-lactam binding to human serum proteins J. Comp. Aided Mol. Design 17:103
Hall L.M., Hall L.H., Kier L.B., 2003 Modeling drug albumin binding affinity with E-state topological structure representation J. Chem. Inf. Comput. Sci. 43:2120
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Liu, J., Yang, L., Li, Y. et al. Prediction of plasma protein binding of drugs using Kier–Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses. J Comput Aided Mol Des 19, 567–583 (2005). https://doi.org/10.1007/s10822-005-9012-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-005-9012-4