3D QSAR: current state, scope, and limitations

All evidence suggests that 3D QSAR techniques will continue to make a valuable con-tribution to the computer-assisted analysis of structure-bioactivity relationships. The search for new descriptors of 3D properties of ligands and innovative strategies to investigate the relationships between these properties and bioactivity continues to be a fruitful research enterprise. Increasing information from structural biology will provide valuable feedback to the hypotheses that form the basis of 3D QSAR methods.

[1]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[2]  David J. Giesen,et al.  Solvation Model for Chloroform Based on Class IV Atomic Charges , 1997 .

[3]  Tim Clark,et al.  A Handbook of Computational Chemistry: A Practical Guide to Chemical Structure and Energy Calculations , 1985 .

[4]  B. V. Reddy,et al.  Recognition of promoter DNA by subdomain 4.2 of Escherichia coli sigma 70: a knowledge based model of -35 hexamer interaction with 4.2 helix-turn-helix motif. , 1997, Journal of biomolecular structure & dynamics.

[5]  Gabriele Cruciani,et al.  Experimental Design in Synthesis Planning and Structure‐Property Correlations , 1995 .

[6]  Paul Geladi,et al.  Interactive variable selection (IVS) for PLS. Part II: Chemical applications , 1995 .

[7]  Romualdo Benigni,et al.  Analysis of Distance Matrices for Studying Data Structures and Separating, Classes , 1993 .

[8]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[9]  P. Hajduk,et al.  Discovering High-Affinity Ligands for Proteins , 1997, Science.

[10]  K Osterlund,et al.  Unexpected binding mode of a cyclic sulfamide HIV-1 protease inhibitor. , 1997, Journal of medicinal chemistry.

[11]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[12]  Yvonne C. Martin,et al.  Use of Structure-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection , 1996, J. Chem. Inf. Comput. Sci..

[13]  Philip M. Dean,et al.  Three-dimensional hydrogen-bond geometry and probability information from a crystal survey , 1996, J. Comput. Aided Mol. Des..

[14]  R. Ornstein,et al.  Binding free energy calculations for P450cam-substrate complexes. , 1996, Protein engineering.

[15]  James H. Wikel,et al.  The use of neural networks for variable selection in QSAR , 1993 .

[16]  E. K. Kemsley,et al.  Discriminant analysis of high-dimensional data: a comparison of principal components analysis and partial least squares data reduction methods , 1996 .

[17]  D. Massart,et al.  Feature selection for the discrimination between pollution types with partial least squares modelling , 1996 .

[18]  Ioan Motoc,et al.  Steric effects in drug design , 1983 .

[19]  W. Dunn,et al.  Genetic Partial Least Squares in QSAR , 1996 .

[20]  Philip Jonathan,et al.  Discriminant analysis with singular covariance matrices. A method incorporating cross‐validation and efficient randomized permutation tests , 1996 .

[21]  L. Kier.  Molecular Orbital Theory In Drug Research , 1971 .

[22]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

[23]  Y. Martin,et al.  Mathematical considerations in series design. , 1979, Journal of medicinal chemistry.

[24]  J. Åqvist,et al.  Cyclic HIV-1 protease inhibitors derived from mannitol: synthesis, inhibitory potencies, and computational predictions of binding affinities. , 1997, Journal of medicinal chemistry.

[25]  Ettore Novellino,et al.  A Critical Review of Recent CoMFA Applications , 1998 .

[26]  Garland R. Marshall,et al.  The Conformational Parameter in Drug Design: The Active Analog Approach , 1979 .

[27]  J. Aqvist,et al.  A new method for predicting binding affinity in computer-aided drug design. , 1994, Protein engineering.

[28]  Garland R. Marshall,et al.  VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands , 1996 .

[29]  James P. Snyder,et al.  Pseudoreceptor Modeling: The Construction of Three-Dimensional Receptor Surrogates , 1995 .

[30]  G R Marshall,et al.  Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of CoMFA models incorporating molecular orbital fields and desolvation free energies based on active-analog and complementary-receptor-field alignment rules. , 1993, Journal of medicinal chemistry.

[31]  H. Kubinyi.  QSAR : Hansch analysis and related approaches , 1993 .

[32]  G. Klopman.  Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .

[33]  G Klebe,et al.  On the prediction of binding properties of drug molecules by comparative molecular field analysis. , 1993, Journal of medicinal chemistry.

[34]  R. Taft,et al.  Solubility properties in biological media 9: prediction of solubility and partition of organic nonelectrolytes in blood and tissues from solvatochromic parameters. , 1987, Journal of pharmaceutical sciences.

[35]  L. Johnson,et al.  Glucose analogue inhibitors of glycogen phosphorylase: from crystallographic analysis to drug prediction using GRID force-field and GOLPE variable selection. , 1995, Acta crystallographica. Section D, Biological crystallography.

[36]  Agnar Höskuldsson,et al.  Quadratic PLS regression , 1992 .

[37]  Tudor I. Oprea,et al.  Three-dimensional quantitative structure-activity relationship of human immunodeficiency virus (I) protease inhibitors. 2. Predictive power using limited exploration of alternate binding modes. , 1994, Journal of medicinal chemistry.

[38]  Eamonn F. Healy,et al.  Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model , 1985 .

[39]  Robert D Clark,et al.  Bioisosterism as a molecular diversity descriptor: steric fields of single "topomeric" conformers. , 1996, Journal of medicinal chemistry.

[40]  A. Doweyko,et al.  The hypothetical active site lattice. An approach to modelling active sites from data on inhibitor molecules. , 1988, Journal of medicinal chemistry.

[41]  Toshio Fujita,et al.  Classical and Three-Dimensional QSAR in Agrochemistry , 1995 .

[42]  T. Halgren,et al.  A priori prediction of activity for HIV-1 protease inhibitors employing energy minimization in the active site. , 1995, Journal of medicinal chemistry.

[43]  Y. Martin,et al.  Quantitative drug design , 1978 .

[44]  J M Blaney,et al.  Electrostatic potential molecular surfaces. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[45]  L. Pauling,et al.  THE NATURE OF THE FORCES BETWEEN ANTIGEN AND ANTIBODY AND OF THE PRECIPITATION REACTION , 1943 .

[46]  Yuichi Kato,et al.  A novel method for superimposing molecules and receptor mapping , 1987 .

[47]  J M Burridge,et al.  Computer graphics in drug design: molecular modeling of thyroid hormone-prealbumin interactions. , 1982, Journal of medicinal chemistry.

[48]  Gerhard Klebe,et al.  Comparative Molecular Similarity Indices Analysis: CoMSIA , 1998 .

[49]  Olga Kennard,et al.  Systematic analysis of structural data as a research technique in organic chemistry , 1983 .

[50]  Matthew Clark,et al.  The Probability of Chance Correlation Using Partial Least Squares (PLS) , 1993 .

[51]  J. Devillers.  Genetic algorithms in molecular modeling , 1996 .

[52]  P. Hajduk,et al.  Discovering High-Affinity Ligands for Proteins: SAR by NMR , 1996, Science.

[53]  M. Charton.  Advances in quantitative structure-property relationships , 1996 .

[54]  D. Covell,et al.  Docking enzyme‐inhibitor complexes using a preference‐based free‐energy surface , 1996, Proteins.

[55]  A. N. Jain,et al.  Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. , 1996, Chemistry & biology.

[56]  L. Hammett,et al.  Physical organic chemistry , 1940 .

[57]  Peter Willett,et al.  Designing bioactive molecules : three-dimensional techniques and applications , 1998 .

[58]  H. Kubinyi.  Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution , 1994 .

[59]  L B Kier,et al.  Sweet taste receptor studies using model interaction energy calculations. , 1974, Journal of pharmaceutical sciences.

[60]  William L. Jorgensen,et al.  Free Energies of Hydration for Organic Molecules from Monte Carlo Simulations , 1995 .

[61]  D. Case,et al.  Incorporating solvation effects into density functional theory: Calculation of absolute acidities , 1997 .

[62]  K. Liedl,et al.  Different electrostatic descriptors in comparative molecular field analysis: A comparison of molecular electrostatic and coulomb potentials , 1996, J. Comput. Chem..

[63]  Bruce L. Bush,et al.  Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA , 1993, J. Comput. Aided Mol. Des..

[64]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[65]  Robert P. Sheridan,et al.  Using a Genetic Algorithm To Suggest Combinatorial Libraries , 1995, J. Chem. Inf. Comput. Sci..

[66]  Calculations of the electrostatic free energy contributions to the binding free energy of sulfonamides to carbonic anhydrase , 1996 .

[67]  C. Braak,et al.  Prediction error in partial least squares regression: a critique on the deviation used in The Unscrambler , 1995 .

[68]  G. Jolles,et al.  Drug design : fact or fantasy? , 1984 .

[69]  Edward E. Hodgkin,et al.  Molecular similarity based on electrostatic potential and electric field , 1987 .

[70]  Gerhard Klebe,et al.  Different approaches toward an automatic structural alignment of drug molecules: Applications to sterol mimics, thrombin and thermolysin inhibitors , 1994, J. Comput. Aided Mol. Des..

[71]  Y. Martin,et al.  Direct prediction of dissociation constants (pKa's) of clonidine-like imidazolines, 2-substituted imidazoles, and 1-methyl-2-substituted-imidazoles from 3D structures using a comparative molecular field analysis (CoMFA) approach. , 1991, Journal of medicinal chemistry.

[72]  Yvonne C. Martin,et al.  The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding , 1997, J. Chem. Inf. Comput. Sci..

[73]  Sung Jin Cho,et al.  Cross-Validated R2 Guided Region Selection for CoMFA Studies , 1998 .

[74]  Sherry L. Mowbray,et al.  Sugar Recognition by a Glucose/Galactose Receptor , 1995, The Journal of Biological Chemistry.

[75]  A. Hopfinger,et al.  3D QSAR of Flexible Molecules Using Tensor Representation , 1998 .

[76]  P J Goodford,et al.  Physicochemical-activity relationship in practice. 2. Rational selection of benzenoid substituents. , 1975, Journal of medicinal chemistry.

[77]  Hans-Joachim Böhm,et al.  The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[78]  Donald G. Truhlar,et al.  AM1-SM2 and PM3-SM3 parameterized SCF solvation models for free energies in aqueous solution , 1992, J. Comput. Aided Mol. Des..

[79]  R. Wade,et al.  Prediction of drug binding affinities by comparative binding energy analysis , 1995 .

[80]  Ajay,et al.  Computational methods to predict binding free energy in ligand-receptor complexes. , 1995, Journal of medicinal chemistry.

[81]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[82]  Igor V. Tetko,et al.  Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..

[83]  T. Hansson,et al.  Estimation of binding free energies for HIV proteinase inhibitors by molecular dynamics simulations. , 1995, Protein engineering.

[84]  R Langridge,et al.  Real-time color graphics in studies of molecular interactions. , 1981, Science.

[85]  S. Free,et al.  A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.

[86]  Igor V. Tetko,et al.  Neural Network Studies, 2. Variable Selection , 1996, J. Chem. Inf. Comput. Sci..

[87]  J. Andrew McCammon,et al.  Comparison of Continuum and Explicit Models of Solvation: Potentials of Mean Force for Alanine Dipeptide , 1996 .

[88]  Ajay N. Jain.  Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities , 1996, J. Comput. Aided Mol. Des..

[89]  Yvonne C. Martin,et al.  Use of molecular fields to compare series of potentially bioactive molecules designed by scientists or by computer , 1990 .

[90]  Hxugo Kubiny.  Variable Selection in QSAR Studies. I. An Evolutionary Algorithm , 1994 .

[91]  A. Klamt,et al.  COSMO : a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient , 1993 .

[92]  N. Bodor,et al.  Neural network studies: Part 3. Prediction of partition coefficients , 1994 .

[93]  Mathew Hahn,et al.  Receptor Surface Models , 1998 .

[94]  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.

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

[96]  Zbigniew Michalewicz,et al.  Evolutionary algorithms , 1997, Emerging Evolutionary Algorithms for Antennas and Wireless Communications.

[97]  Gabriele Cruciani,et al.  GOLPE-Guided Region Selection , 1998 .

[98]  P. Goodford.  A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[99]  H. Kubinyi.  QSAR: Hansch Analysis and Related Approaches: Kubinyi/QSAR , 1993 .

[100]  J. Scott Dixon,et al.  A good ligand is hard to find: Automated docking methods , 1993 .

[101]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists , 1997, J. Chem. Inf. Comput. Sci..

[102]  Tudor I. Oprea,et al.  Three-dimensional QSAR of human immunodeficiency virus (I) protease inhibitors. 1. A CoMFA study employing experimentally-determined alignment rules. , 1993, Journal of medicinal chemistry.

[103]  Daniel E. Platt,et al.  Comparative molecular moment analysis (CoMMA) , 1998 .

[104]  Garland R. Marshall,et al.  3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries , 1993 .

[105]  Peter Willett,et al.  Rapid Quantification of Molecular Diversity for Selective Database Acquisition , 1997, J. Chem. Inf. Comput. Sci..

[106]  A. Hopfinger.  Computer-assisted drug design. , 1985, Journal of medicinal chemistry.

[107]  R. Glen,et al.  Computer-aided design and synthesis of 5-substituted tryptamines and their pharmacology at the 5-HT1D receptor: discovery of compounds with potential anti-migraine properties. , 1995, Journal of medicinal chemistry.

[108]  Ettore Novellino,et al.  COMBINED USE OF FACTORIAL DESIGN AND COMPARATIVE MOLECULAR FIELD ANALYSIS (COMFA) : A CASE STUDY , 1994 .

[109]  Frank H. Allen,et al.  Correlation of the hydrogen‐bond acceptor properties of nitrogen with the geometry of the Nsp2→Nsp3 transition in R1(X=)C–NR2R3 substructures: reaction pathway for the protonation of nitrogen , 1995 .

[110]  E. Shakhnovich,et al.  SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence , 1996 .

[111]  A. Doweyko,et al.  Three-dimensional pharmacophores from binding data. , 1994, Journal of medicinal chemistry.

[112]  Ulf Norinder,et al.  Recent Progress in CoMFA Methodology and Related Techniques , 1998 .

[113]  Klaus R. Liedl,et al.  Improving the Predictive Quality of CoMFA Models , 1998 .

[114]  D. Manallack,et al.  Statistics using neural networks: chance effects. , 1993, Journal of medicinal chemistry.

[115]  Ulf Norinder,et al.  Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes , 1990, J. Comput. Aided Mol. Des..

[116]  Edgar F. Meyer,et al.  Backward binding and other structural surprises , 1995 .

[117]  P. Kollman.  Advances and Continuing Challenges in Achieving Realistic and Predictive Simulations of the Properties of Organic and Biological Molecules , 1996 .

[118]  A. Hopfinger.  A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis , 1980 .

[119]  P. Dirac.  Quantum Mechanics of Many-Electron Systems , 1929 .

[120]  Alan B. Forsythe,et al.  Strategy in drug design. Cluster anlysis as an aid in the selection of substituents , 1973 .

[121]  R L Jernigan,et al.  A preference‐based free‐energy parameterization of enzyme‐inhibitor binding. Applications to HIV‐1‐protease inhibitor design , 1995, Protein science : a publication of the Protein Society.

[122]  Y. Martin,et al.  PLS analysis of distance matrices to detect nonlinear relationships between biological potency and molecular properties. , 1995, Journal of medicinal chemistry.

[123]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .