Integration of virtual and high-throughput screening (original) (raw)

• Handen, J. S. High-throughput screening — challenges for the future. Drug Discov. World 47–50 (Summer 2002).
• Fox, S., Farr-Jones, S. & Yund, M. A. High-throughput screening for drug discovery: continually transitioning into new technologies. J. Biomol. Screen. 4, 183–186 (1999).
  Article CAS PubMed Google Scholar
• Smith, A. Screening for drug discovery: the leading question. Nature 418, 453–459 (2002).
  PubMed Google Scholar
• Fox, S., Farr-Jones, S., Sopchak, L. & Wang, H. Fine-tuning the technology strategies for lead finding. Drug Discov. World 24–30 (Summer 2002).
• Bajorath, J. Rational drug discovery revisited: interfacing experimental programs with bio- and chemo-informatics. Drug Discov. Today 6, 989–995 (2001).
  CAS PubMed Google Scholar
• Drews, J. Drug discovery: a historical perspective. Science 287, 1960–1964 (2000).
  CAS PubMed Google Scholar
• Bajorath, J. Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J. Chem. Inf. Comput. Sci. 41, 233–245 (2001).
  CAS PubMed Google Scholar
• Bajorath, J. Virtual screening: methods, expectations, and reality. Curr. Drug Discov. 2, 24–28 (2002).
  Google Scholar
• Brown, F. K. Chemoinformatics: what is it and how does it impact drug discovery. Annu. Rep. Med. Chem. 33, 375–384 (1998).
  CAS Google Scholar
• Agrafiotis, D. K., Lobanov, V. S. & Salemme, R. F. Combinatorial informatics in the post-genomics era. Nature Rev. Drug Discov. 1, 337–346 (2002). An excellent review of diversity analysis, library design and profiling methods.
  CAS Google Scholar
• Kuntz, I. D. Structure-based strategies for drug design and discovery. Science 257, 1078–1082 (1992).
  CAS PubMed Google Scholar
• Halpering, I., Ma, B., Wolfson, H. & Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47, 409–443 (2002).
  Google Scholar
• Willett, P., Barnard, J. M. & Downs, G. M. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38, 983–996 (1998). This manuscript provides an introduction to similarity searching and a good description of different similarity metrics.
  CAS Google Scholar
• Livingstone, D. J. The characterization of chemical structures using molecular properties. A survey. J. Chem. Inf. Comput. Sci. 40, 195–209 (2000). An extensive review of different types of molecular property descriptor.
  CAS PubMed Google Scholar
• Cramer, R. D., Redl, G. & Berkoff, C. E. Substructural analysis. A novel approach to the problem of drug design. J. Med. Chem. 17, 533–535 (1974).
  CAS PubMed Google Scholar
• Barnard, J. M. Substructure searching methods. Old and new. J. Chem. Inf. Comput. Sci. 33, 532–538 (1993).
  CAS Google Scholar
• Gund, P. in Progress in Molecular and Subcellular Biology Vol. 5 (ed. Hahn, F. E.) 117–142 (Springer–Verlag, Berlin, 1977).
  Google Scholar
• Sheridan, R. P., Rusinko, A., Nilakantan, R. & Venkataraghavan, R. Searching for pharmacophores in large coordinate databases and its use in drug design. Proc. Natl Acad. Sci. USA 86, 8156–8159 (1989).
  Google Scholar
• Martin, Y. C. 3D database searching in drug design. J. Med. Chem. 35, 2145–2154 (1992).
  CAS PubMed Google Scholar
• Pearlman, R. S. Rapid generation of high quality approximate 3D molecular structures. Chem. Des. Auto. News 2, 1–7 (1987).
  Google Scholar
• Gasteiger, J., Rudolph, C. & Sadowski, J. Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comp. Method. 3, 537–547 (1990).
  CAS Google Scholar
• Cramer, R. D. et al. Prospective identification of biologically active structures by topomer similarity searching. J. Med. Chem. 42, 3919–3933 (1999).
  PubMed Google Scholar
• Andrews, K. M. & Cramer, R. D. Toward general methods for targeted library design: topomer shape similarity with diverse structures as queries. J. Med. Chem. 43, 1723–1740 (2000).
  CAS PubMed Google Scholar
• Hall, L. H. & Kier, L. B. The E-state as the basis for molecular structure space definition and structure similarity. J. Chem. Inf. Comput. Sci. 40, 784–791 (2000).
  CAS PubMed Google Scholar
• Kier, L. B. & Hall, L. H. Database organization and searching with E-state indices. SAR QSAR Environ. Res. 12, 55–74 (2001).
  CAS PubMed Google Scholar
• Hull, R. D. et al. Latent semantic structure indexing (LaSSI) for defining chemical similarity. J. Med. Chem. 44, 1177–1184 (2001).
  CAS PubMed Google Scholar
• Raymond, J. W. & Willett, P. Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. J. Comput. Aided Mol. Des. 16, 59–71 (2002).
  CAS PubMed Google Scholar
• Cramer, R. D., Patterson, D. E. & Bunce, J. D. Comparative molecular field analysis (CoMFA). Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959–5967 (1988).
  CAS PubMed Google Scholar
• Hopfinger, A. J. et al. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J. Am. Chem. Soc. 119, 10509–10524 (1997).
  CAS Google Scholar
• Duca, J. S. & Hopfinger, A. J. Estimation of molecular similarity based on 4D-QSAR analysis: formalism and validation. J. Chem. Inf. Comput. Sci. 41, 1367–1387 (2001).
  CAS PubMed Google Scholar
• Hopfinger, A. J., Reaka, A., Venkatarangan, P., Duca, J. S. & Wang, S. Construction of a virtual high throughput screen by 4D-QSAR analysis: application to a combinatorial library of glucose inhibitors of glycogen phosphorylase b. J. Chem. Inf. Comput. Sci. 39, 1151–1160 (1999). An instructive example of the adoption of a multidimensional QSAR model for VS calculations.
  CAS Google Scholar
• Xue, L., Godden, J. W. & Bajorath, J. Evaluation of descriptors and mini-fingerprints for the identification of molecules with similar activity. J. Chem. Inf. Comput. Sci. 40, 1227–1234 (2000).
  CAS PubMed Google Scholar
• Xue, L., Stahura, F. L., Godden, J. W. & Bajorath, J. Mini-fingerprints detect similar activity of receptor ligands previously recognized only by three-dimensional pharmacophore-based methods. J. Chem. Inf. Comput. Sci. 41, 394–401 (2001). This paper shows that conceptually simple but carefully designed 2D fingerprints can recognize molecules that have diverse structures but similar activity.
  CAS PubMed Google Scholar
• Mason, J. S. et al. New 4-point pharmacophore method for molecular similarity and diversity applications: overview over the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J. Med. Chem. 42, 3251–3264 (1999). An extensive introduction to the four-point pharmacophore methodology.
  CAS PubMed Google Scholar
• Mason, J. S. & Cheney, D. L. Library design and virtual screening using multiple point pharmacophore fingerprints. Pac. Symp. Biocomput. 5, 576–587 (2000).
  Google Scholar
• McGregor, M. J. & Muskal, S. M. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Inf. Comput. Sci. 39, 569–574 (1999).
  CAS PubMed Google Scholar
• Bradley, E. K. et al. A rapid computational method for lead evolution: description and application to α1-adrenergic antagonists. J. Med. Chem. 43, 2770–2774 (2000).
  CAS PubMed Google Scholar
• Brown, R. D. & Martin, Y. C. Use of structure–activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci. 36, 572–584 (1996).
  CAS Google Scholar
• Brown, R. D. & Martin, Y. C. The information content of 2D and 3D molecular descriptors relevant to ligand–receptor binding. J. Chem. Inf. Comput. Sci. 37, 731–740 (1997).
  Google Scholar
• Matter, H. Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional descriptors. J. Med. Chem. 40, 1219–1229 (1997).
  CAS PubMed Google Scholar
• Willett, P., Wintermann, V. & Bawden, D. Implementation of non-hierarchic cluster analysis methods in chemical information systems: selection of compounds for biological testing and clustering of substructure search output. J. Chem. Inf. Comput. Sci. 26, 109–118 (1986).
  CAS Google Scholar
• Barnard, J. M. & Downs, G. M. Clustering of chemical structures on the basis of two-dimensional similarity measures. J. Chem. Inf. Comput. Sci. 32, 644–649 (1992).
  CAS Google Scholar
• Pearlman, R. S. & Smith, K. M. Novel software tools for chemical diversity. Perspect. Drug Discov. Design 9, 339–353 (1998).
  Google Scholar
• Pearlman, R. S. & Smith, K. M. Metric validation and the receptor-relevant subspace concept. J. Chem. Inf. Comput. Sci. 39, 28–35 (1999). A landmark paper rationalizing the design of low-dimensional reference spaces for cell-based partitioning.
  CAS Google Scholar
• Bayley, M. J. & Willett, P. Binning schemes for partition-based compound selection. J. Mol. Graph. Model. 17, 10–18 (1999).
  CAS PubMed Google Scholar
• Agrafiotis, D. K. & Rassokhin, D. N. A fractal approach for selecting an appropriate bin size for cell-based diversity estimation. J. Chem. Inf. Comput. Sci. 42, 117–122 (2002).
  CAS PubMed Google Scholar
• Xue, L. & Bajorath, J. Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J. Chem. Inf. Comput. Sci. 40, 801–809 (2000).
  CAS PubMed Google Scholar
• Xie, D., Tropsha, A. & Schlick, T. An efficient projection protocol for chemical databases: single value decomposition combined with truncated Newton minimization. J. Chem. Inf. Comput. Sci. 40, 167–177 (2000).
  CAS PubMed Google Scholar
• Godden, J. W., Xue, L. & Bajorath, J. Classification of biologically active compounds by median partitioning. J. Chem. Inf. Comput. Sci. 42, 1263–1269 (2002).
  CAS PubMed Google Scholar
• Sheridan, R. P. & Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discov. Today 7, 903–911 (2002).
  PubMed Google Scholar
• Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).
  CAS Google Scholar
• Hann, M., Hudson, B., Lifely, R., Miller, L. & Ramsden, N. Strategic pooling of compounds for high-throughput screening. J. Chem. Inf. Comput. Sci. 39, 897–902 (1999).
  CAS PubMed Google Scholar
• Lipinski, C. A. Avoiding investments in doomed drugs. Curr. Drug Discov. 1, 17–19 (2001).
  Google Scholar
• Sutter, J. M. & Jurs, P. C. Prediction of aqueous solubility for a diverse set of heteroatom-containing organic compounds using a quantitative structure–property relationship. J. Chem. Inf. Comput. Sci. 36, 100–107 (1996).
  CAS Google Scholar
• Huuskonen, J., Salo, M. & Taskinen, J. Aqueous solubility prediction of drugs based on molecular topology and neural network modeling. J. Chem. Inf. Comput. Sci. 38, 450–456 (1998).
  CAS PubMed Google Scholar
• Klopman, G. & Zhao, H. Estimation of aqueous solubility of organic molecules by the group contribution approach. J. Chem. Inf. Comput. Sci. 41, 439–445 (2001).
  CAS PubMed Google Scholar
• Jorgensen, W. L. & Duffy, E. R. Prediction of drug solubility from structures. Adv. Drug. Deliv. Rev. 54, 355–366 (2002).
  CAS PubMed Google Scholar
• Wessel, M. D., Jurs, P. C., Tolan, J. W. & Muskal, S. M. Prediction of human intestinal absorption of drug compounds from molecular structure. J. Chem. Inf. Comput. Sci. 38, 726–735 (1998).
  CAS PubMed Google Scholar
• Egan, W. J., Merz, K. M. Jr & Baldwin, J. J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43, 3867–3877 (2000).
  CAS PubMed Google Scholar
• Ertl, P., Rohde, B. & Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 43, 3714–3717 (2000).
  CAS PubMed Google Scholar
• Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).
  CAS Google Scholar
• Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).
  CAS PubMed Google Scholar
• Sheridan, R. P. The most common chemical replacements in drug-like compounds. J. Chem. Inf. Comput. Sci. 42, 103–108 (2002).
  CAS PubMed Google Scholar
• Oprea, T. Property distribution of drug-related chemical databases. J. Comput. Aided Mol. Des. 14, 251–264 (2000).
  CAS PubMed Google Scholar
• Ghose, A. K., Viswanadhan, V. N. & Wendoloski, J. J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1, 55–67 (1999).
  CAS PubMed Google Scholar
• Muegge, I., Heald, S. L. & Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 44, 1841–1846 (2001).
  CAS PubMed Google Scholar
• Gillet, V. J., Willett, P. & Bradshaw, J. Identification of biological activity profiles using substructural analysis and genetic algorithms. J. Chem. Inf. Comput. Sci. 38, 165–179 (1998). A good example of the usefulness of genetic algorithms in descriptor analysis. Here, a genetic algorithm implementation was used to assign weighting factors to molecular descriptors for the prediction of drug-like molecules.
  CAS PubMed Google Scholar
• Ajay, A., Walters, W. P. & Murcko, M. A. Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules? J. Med. Chem. 41, 3314–3324 (1998).
  CAS PubMed Google Scholar
• Sadowski, J. & Kubinyi, H. A scoring scheme to distinguish between drugs and non-drugs. J. Med. Chem. 41, 3325–3329 (1998). References 68 and 69 were the first to apply machine-learning techniques to the systematic prediction of drug-likeness. Different from QSAR-type analysis, neural network models can capture non-linear property relationships.
  CAS PubMed Google Scholar
• Norinder, U., Sjöberg, P. & Österberg, T. Theoretical calculation and prediction of blood–brain-barrier partitioning of organic solutes using MolSurf parameterization and PLS statistics. J. Pharm. Sci. 87, 952–959 (1998).
  CAS PubMed Google Scholar
• van de Waterbeemd, H., Camenisch, G., Folkers, G., Chretien, J. R. & Raevsky, O. A. Estimation of blood–brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J. Drug Target. 6, 151–165 (1998).
  CAS PubMed Google Scholar
• Kelder, J., Grootenhuis, P. D., Bayada, D. M., Delbressine, L. P. & Ploemen, J. P. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm. Res. 16, 1514–1519 (1999).
  CAS PubMed Google Scholar
• Ajay, A., Bemis, G. W. & Murcko, M. A. Designing libraries with CNS activity. J. Med. Chem. 42, 4942–4951 (1999).
  CAS PubMed Google Scholar
• Caldwell, G. W., Ritchie, M. M., Masucci, J. A., Hagemann, W. & Yan, Z. The new pre-clinical paradigm: compound optimization in early and late phase drug discovery. Curr. Topics Med. Chem. 1, 353–366 (2001).
  CAS Google Scholar
• Yoshida, F. & Topliss, J. G. QSAR model for drug human oral bioavailability. J. Med. Chem. 43, 2575–2585 (2000).
  CAS PubMed Google Scholar
• de Groot, M. J., Ackland, M. J., Horne, V. A., Alex, A. A. & Jones, B. C. A novel approach to predicting P450 mediated drug metabolism. CYP2D6 catalyzed _N_-dealkylation reactions and qualitative metabolite predictions using a combined protein and pharmacophore model for CYP2D6. J. Med. Chem. 42, 1515–1524 (1999).
  CAS PubMed Google Scholar
• Ekins, S. et al. Three- and four-dimensional quantitative structure–activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab. Dispos. 28, 994–1002 (2000).
  CAS PubMed Google Scholar
• Jones, J. P., Mysinger, M. & Korzekwa, K. R. Computational models for cytochrome P450: a predictive electronic model for aromatic oxidation and hydrogen atom abstraction. Drug Metab. Dispos. 30, 7–12 (2002).
  CAS PubMed Google Scholar
• Ahlberg, C. Visual exploration of HTS databases: bridging the gap between chemistry and biology. Drug Discov. Today 4, 370–376 (1999).
  CAS PubMed Google Scholar
• Engels, M. F., Wouters, L., Verbeeck, R. & Vanhoof, G. Outlier mining in high throughput screening experiments. J. Biomol. Screen. 7, 341–351 (2002).
  CAS PubMed Google Scholar
• Chen, X., Rusinko, A. & Young, S. S. Recursive partitioning analysis of a large structure–activity data set using three-dimensional descriptors. J. Chem. Inf. Comput. Sci. 38, 1054–1062 (1998).
  CAS Google Scholar
• Rusinko, A., Farmen, M. W., Lambert, C. G., Brown, P. L. & Young, S. S. Analysis of a large structure–biological activity data set using recursive partitioning. J. Chem. Inf. Comput. Sci. 39, 1017–1026 (1999). References 81 and 82 establish the recursive partitioning approach for the analysis and mining of large screening data sets.
  CAS PubMed Google Scholar
• Cho, S. J., Shen, C. F. & Hermsmeier, M. A. Binary formal inference-based recursive modeling using multiple atom and physicochemical property class pair and torsion descriptors as decision criteria. J. Chem. Inf. Comput. Sci. 40, 668–680 (2000).
  CAS PubMed Google Scholar
• van Rhee, A. M. et al. Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning. J. Comb. Chem. 3, 267–277 (2001).
  CAS PubMed Google Scholar
• Miller, D. A. Results of a new classification algorithm combining K nearest neighbors and recursive partitioning. J. Chem. Inf. Comput. Sci. 41, 168–175 (2001).
  CAS PubMed Google Scholar
• Blower, P., Fligner, M., Verducci, J. & Bjoraker, J. On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds. J. Chem. Inf. Comput. Sci. 42, 393–404 (2002).
  CAS PubMed Google Scholar
• Nicolaou, C. A., Tamura, S. Y., Kelley, B. P., Bassett, S. I. & Nutt, R. F. Analysis of large screening data sets via adaptively grown phylogenetic-like trees. J. Chem. Inf. Comput. Sci. 42, 1069–1079 (2002). The introduction of a new clustering method that shows promise in extracting diverse structure–activity relationships from screening data.
  CAS PubMed Google Scholar
• Tamura, S. Y., Bacha, P. A., Gruver, H. S. & Nutt, R. F. Data analysis of high-throughput screening results: application of multidomain clustering to the NCI anti-HIV. J. Med. Chem. 45, 3082–3093 (2002).
  CAS PubMed Google Scholar
• Menard, P. R., Lewis, R. A. & Mason, J. S. Rational screening set design and compound selection: cascaded clustering. J. Chem. Inf. Comput. Sci. 38, 497–505 (1998).
  CAS Google Scholar
• Rosenkranz, H. S. et al. Development, characterization and application of predictive-toxicology models. SAR QSAR Environ. Res. 10, 277–298 (1999).
  CAS PubMed Google Scholar
• Roberts, G., Myatt, G. J., Johnson, W. P., Cross, K. P. & Blower, P. LeadScope: software for exploring large sets of screening data. J. Chem. Inf. Comput. Sci. 40, 1302–1314 (2000).
  CAS PubMed Google Scholar
• Labute, P. Binary QSAR: a new method for the determination of quantitative structure activity relationships. Pac. Symp. Biocomput. 4, 444–455 (1999).
  Google Scholar
• Gao, H. Application of BCUT metrics and genetic algorithm in binary QSAR analysis. J. Chem. Inf. Comput. Sci. 41, 402–407 (2001).
  CAS PubMed Google Scholar
• Gao, H., Williams, C., Labute, P. & Bajorath, J. Binary quantitative structure–activity relationship (QSAR) analysis of estrogen receptor ligands. J. Chem. Inf. Comput. Sci. 39, 164–168 (1999).
  CAS PubMed Google Scholar
• Stahura, F. L., Godden, J. W., Xue, L. & Bajorath, J. Distinguishing between natural products and synthetic molecules by Shannon descriptor entropy analysis and binary QSAR calculations. J. Chem. Inf. Comput. Sci. 40, 1245–1252 (2000).
  CAS PubMed Google Scholar
• Stahura, F. L., Godden, J. W. & Bajorath, J. Differential Shannon entropy analysis identifies molecular descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations. J. Chem. Inf. Comput. Sci. 42, 550–558 (2002).
  CAS PubMed Google Scholar
• Harper, G., Bradshaw, J., Gittin, J. C., Green, D. V. S. & Leach, A. R. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J. Chem. Inf. Comput. Sci. 41, 1295–1300 (2001).
  CAS PubMed Google Scholar
• Doman, T. N. et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem. 45, 2213–2221 (2002). One of very few case studies that directly compares the performance of VS and HTS analysis.
  CAS PubMed Google Scholar
• Singh, J. et al. Identification of potent and novel α4β1 antagonists using in silico screening. J. Med. Chem. 45, 2988–2993 (2002).
  CAS PubMed Google Scholar
• Gr¨neberg, S., Stubbs, M. T. & Klebe, G. Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J. Med. Chem. 45, 3588–3602 (2002).
  Google Scholar
• Stahura, F. L., Xue, L., Godden, J. W. & Bajorath, J. Methods for compound selection focused on hits and application in drug discovery. J. Mol. Graph. Model. 20, 439–446 (2002).
  CAS PubMed Google Scholar
• Manallack, D. T. et al. Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks. J. Chem. Inf. Comput. Sci. 42, 1256–1262 (2002).
  CAS PubMed Google Scholar
• Valler, M. J. & Green, D. Diversity screening versus focused screening in drug discovery. Drug Discov. Today 5, 286–293 (2000).
  CAS PubMed Google Scholar
• Martin, Y. C., Kofron, J. L. & Traphagen, L. M. Do structurally similar molecules have similar biological activity? J. Med. Chem. 45, 4350–4358 (2002).
  CAS PubMed Google Scholar
• Engels, M. F. M. & Venkatarangan, P. Smart screening: approaches to efficient HTS. Curr. Opin. Drug Discov. Develop. 4, 275–283 (2001). An instructive description of a sequential-screening strategy, including several interesting benchmark calculations.
  CAS Google Scholar
• Engels, M. F. M., Thielemans, T., Verbinnen, D., Tollenaere, J. P. & Verbeeck, R. CerBeruS: a system supporting the sequential screening process. J. Chem. Inf. Comput. Sci. 40, 241–245 (2000).
  CAS PubMed Google Scholar
• Jones-Hertzog, D. K., Mukhopadhyay, P., Keefer, C. E. & Young, S. S. Use of recursive partitioning in the sequential screening of G protein-coupled receptors. J. Pharmacol. Toxicol. Methods 42, 207–215 (1999).
  CAS PubMed Google Scholar
• Kauvar, L. M. et al. Predicting ligand binding to proteins by affinity fingerprinting. Chem. Biol. 2, 107–118 (1995).
  CAS PubMed Google Scholar
• Dixon, S. L. & Villar, H. O. Bioactive diversity and screening library selection via affinity fingerprinting. J. Chem. Inf. Comput. Sci. 38, 1192–1203 (1998). This paper describes the application of affinity fingerprints in iterative screening situations and provides insights into the predictive value of this approach.
  CAS PubMed Google Scholar
• McGovern, S. L., Caselli, E., Grigorieff, N. & Shoichet, B. K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).
  CAS PubMed Google Scholar
• Powers, R. A., Morandi, F. & Shoichet, B. K. Structure-based discovery of a novel, noncovalent inhibitor AmpC β-lactamase. Structure 10, 1013–1023 (2002).
  CAS PubMed Google Scholar
• Sotriffer, C. A., Gohlke, H. & Klebe, G. Docking into knowledge-based potential fields: a comparative evaluation of DrugScore. J. Med. Chem. 45, 1967–1970 (2002).
  CAS PubMed Google Scholar
• Wei, B., Baase, W., Weaver, L. Matthews & Shoichet, B. K. A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 322, 339–355 (2002). A well-designed study that uses T4 lysozyme mutant structures as a versatile model system for the evaluation of docking and scoring functions.
  CAS PubMed Google Scholar