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Nature Reviews Drug Discovery volume 4, pages 649–663 (2005)Cite this article
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Molecular de novo design, which involves incremental construction of a ligand model within a model of the receptor or enzyme active site, produces novel molecular structures with desired pharmacological properties from scratch.
De novo molecule-design software is confronted with a virtually infinite search space. As such a large space prohibits exhaustive searching, navigation in the de novo design process relies on the principle of local optimization.
Basically, three questions have to be addressed by a de novo design program: how to assemble the candidate compounds; how to evaluate their potential quality; and how to sample the search space effectively.
This review gives an overview of computer-based molecular de novo design methods on a conceptual level, considering these three questions, and focusing on the design of small, drug-like molecules. Successful examples of de novo design in the hit- and lead-finding stages of the drug discovery process are used to show that de novo design provides a method for lead identification.
De novo design can therefore be regarded as a complement to other virtual techniques, such as database searching, and non-virtual techniques such as high-throughput screening. We also accentuate strengths and weaknesses of current de novo design approaches.
Ever since the first automated de novo design techniques were conceived only 15 years ago, the computer-based design of hit and lead structure candidates has emerged as a complementary approach to high-throughput screening. Although many challenges remain, de novo design supports drug discovery projects by generating novel pharmaceutically active agents with desired properties in a cost- and time-efficient manner. In this review, we outline the various design concepts and highlight current developments in computer-based de novo design.
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1fkf
Dobson, C. M. Chemical space and biology. Nature 432, 824–828 (2004).
Article  CAS  PubMed  Google Scholar 
Lipinski, C. & Hopkins, A. Navigating chemical space for biology and medicine. Nature 432, 855–861 (2004).
Article  CAS  PubMed  Google Scholar 
Schneider, G. Trends in virtual combinatorial library design. Curr. Med. Chem. 9, 2095–2101 (2002).
Article  CAS  PubMed  Google Scholar 
Richardson, J. S. & Richardson, D. C. The de novo design of protein structures. Trends Biochem. Sci. 14, 304–309 (1989).
Article  CAS  PubMed  Google Scholar 
Richardson, J. S. et al. Looking at proteins: representations, folding, packing, and design. Biophys. J. 63, 1185–1209 (1992).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Moon, J. B. & Howe, W. J. Computer design of bioactive molecules: a method for receptor-based de novo ligand design. Proteins 11, 314–328 (1991).
Article  CAS  PubMed  Google Scholar 
Schneider, G. & Wrede, P. The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site. Biophys. J. 66, 335–344 (1994).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Schneider, G. et al. Peptide design by artificial neural networks and computer-based evolutionary search. Proc. Natl Acad. Sci. USA 95, 12179–12184 (1998).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Venkatasubramanian, V., Chan, K. & Caruthers, J. M. Computer-aided molecular design using genetic algorithms. Computers Chem. Eng. 18, 833–844 (1994).
Article  CAS  Google Scholar 
Venkatasubramanian, V., Sundaram, A., Chan, K. & Caruthers, J. M. in Genetic Algorithms in Molecular Modelling (ed. Devillers, J.) 271–302 (Academic, London, 1996).
Book  Google Scholar 
Sundaram, A. & Venkatasubramanian, V. Parametric sensitivity and search-space characterization studies of genetic algorithms for computer-aided polymer design. J. Chem. Inf. Comput. Sci. 38, 1177–1191 (1998).
Article  CAS  Google Scholar 
Danziger, D. J. & Dean, P. M. Automated site-directed drug design: a general algorithm for knowledge acquisition about hydrogen-bonding regions at protein surfaces. Proc R Soc Lond B Biol Sci B 236, 101–113 (1989). First work about interaction site derivation from a receptor structure tailored for the use in automated de novo design.
PubMed  Google Scholar 
Böhm, H. -J. The computer program LUDI: a new simple method for the de-novo design of enzyme inhibitors. J. Comput. Aided Mol. Des. 6, 61–78 (1992).
Article  PubMed  Google Scholar 
Böhm, H. -J. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des. 6, 593–606 (1992).
Article  PubMed  Google Scholar 
Clark, D. E. et al. PRO LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules. J. Comput. Aided Mol. Des. 9, 13–32 (1995). A comprehensive approach that adopts a lot of earlier ideas and provides new concepts.
Article  CAS  PubMed  Google Scholar 
Murray, C. W. et al. PRO_SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology. J. Comp. Aided Mol. Des. 11, 193–207 (1997).
Article  CAS  Google Scholar 
Gillett, V. J., Myatt, G., Zsoldos, Z. & Johnson, A. P. SPROUT, HIPPO and CAESA: tools for de novo structure generation and estimation of synthetic accessibility. Perspect. Drug Discov. Des. 3, 34–50 (1995).
Article  Google Scholar 
Rotstein, S. H. & Murcko, M. A. GroupBuild: a fragment-based method for de novo drug design. J. Med. Chem. 36, 1700–1710 (1993).
Article  CAS  PubMed  Google Scholar 
Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).
Article  CAS  PubMed  Google Scholar 
Nishibata, Y. & Itai, A. Automatic creation of dug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 47, 8985–8990 (1991).
Article  CAS  Google Scholar 
Bohacek, R. S. & McMartin, C. Multiple highly diverse structures complementary to enzyme binding sites: results of extensive application of a de novo design method incorporating combinatorial growth. J. Am. Chem. Soc. 116, 5560–5571 (1994).
Article  CAS  Google Scholar 
Glen, R. C. & Payne, A. W. R. A genetic algorithm for the automated generation of molecules within constraints. J. Comput. Aided. Mol. Des. 9, 181–202 (1995).
Article  CAS  PubMed  Google Scholar 
Luo, Z., Wang, R. & Lai, L. RASSE: a new method for structure-based drug design. J. Chem. Inf. Comput. Sci. 36, 1187–1194 (1996).
Article  CAS  PubMed  Google Scholar 
Wang, R., Gao, Y. & Lai, L. LigBuilder: a multi-purpose program for structure-based drug design. J. Mol. Model. 6, 498–516 (2000).
Article  CAS  Google Scholar 
Eisen, M. B., Wiley, D. C., Karplus, M. & Hubbard, R. E. HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins 19, 199–221 (1994).
Article  CAS  PubMed  Google Scholar 
Miranker, A. & Karplus, M. An automated method for dynamic ligand design. Proteins 23, 472–490 (1995).
Article  CAS  PubMed  Google Scholar 
Miranker, A. & Karplus, M. Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11, 29–34 (1991).
Article  CAS  PubMed  Google Scholar 
Lewis, R. A. et al. Automated site-directed drug design using molecular lattices. J. Mol. Graphics 10, 66–78 (1992).
Article  CAS  Google Scholar 
Roe, D. C. & Kuntz, I. D. BUILDER v.2: improving the chemistry of a de novo design strategy. J. Comput. Aided Mol. Des. 9, 269–282 (1995).
Article  CAS  PubMed  Google Scholar 
Tschinke, V. & Cohen, N. C. The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypothesis. J. Med. Chem. 36, 3863–3870 (1993).
Article  CAS  PubMed  Google Scholar 
Lewis, R. A. & Dean, P. M. Automated site-directed drug design: the formation of molecular templates in primary structure generation. Proc R Soc Lond B Biol Sci 236, 141–162 (1989).
Article  CAS  PubMed  Google Scholar 
Gillett, V. A., Johnson, A. P., Mata, P. & Sike, S. Automated structure design in 3D. Tetrahedron Comput. Method. 3, 681–696 (1990).
Article  Google Scholar 
Lewis, R. A. Automated site-directed drug design: approaches to the formation of 3D molecular graphs. J. Comput. Aided Mol. Des. 4, 205–210 (1990).
Article  CAS  PubMed  Google Scholar 
Rotstein, S. H. & Murcko, M. A. GenStar: a method for de novo drug design. J. Comput. Aided. Mol. Des. 7, 23–43 (1993).
Article  CAS  PubMed  Google Scholar 
Pearlman, D. A. & Murcko, M. A. CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. J. Med. Chem. 39, 1651–1663 (1996). Introduces the concept of consensus molecular dynamics as a method for structure sampling to de novo design.
Article  CAS  PubMed  Google Scholar 
Liu, H., Duan, Z., Luo, Q. & Shi, Y. Structure-based ligand design by dynamically assembling molecular building blocks at binding site. Proteins 36, 462–470 (1999).
Article  CAS  PubMed  Google Scholar 
Zhu, J., Yu, H., Fan, H. Liu, H. & Shi, Y. Design of selective inhibitors of cyclooxygenase-2 dynamic assembly of molecular building blocks. J. Comput. Aided Mol. Des. 15, 447–463 (2001).
Article  CAS  PubMed  Google Scholar 
Zhu, J., Fan, H., Liu, H. & Shi, Y. Structure-based ligand design for flexible proteins: application of new F-DycoBlock. J. Comput. Aided Mol. Des. 15, 979–996 (2001).
Article  CAS  PubMed  Google Scholar 
Pearlman, D. A. & Murcko, M. A. CONCEPTS: new dynamic algorithm for de novo design suggestion. J. Comput. Chem. 14, 1184–1193 (1993).
Article  CAS  Google Scholar 
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V. & Mee, R. P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des. 11, 425–445 (1997).
Article  CAS  PubMed  Google Scholar 
DeWitte, R. S. & Shakhnovich, E. I. SMoG de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J. Am. Chem. Soc. 118, 11733–11744 (1996).
Article  CAS  Google Scholar 
Ishchenko, A. V. & Shakhnovich, E. I. SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein–ligand interactions. J. Med. Chem. 45, 2770–2780 (2002).
Article  CAS  PubMed  Google Scholar 
Wise, A., Gearing, K. & Rees, S. Target validation of G-protein coupled receptors. Drug Discov. Today 7, 235–246 (2002).
Article  CAS  PubMed  Google Scholar 
Waszkowycz, B. et al. PRO LIGAND: an approach to de novo molecular design. 2. design of novel molecules from molecular field analysis (MFA) models and pharmacophores. J. Med. Chem. 37, 3994–4002 (1994).
Article  CAS  PubMed  Google Scholar 
Nachbar, R. B. Molecular evolution: automated manipulation of hierarchical chemical topology and its application to average molecular structures. Genet. Programming Evolvable Machines 1, 57–94 (2000). Development of genetic operators for graph-based structure sampling and detailed description of the problems that have to be solved.
Article  Google Scholar 
Pellegrini, E. & Field, M. J. Development and testing of a de novo drug-design algorithm. J. Comp. Aided Mol. Des. 17, 621–641 (2003).
Article  CAS  Google Scholar 
Douguet, D., Thoreau, E. & Grassy, G. A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm. J. Comput. Aided Mol. Des. 14, 449–466 (2000).
Article  CAS  PubMed  Google Scholar 
Schneider, G., Lee, M. -L., Stahl, M. & Schneider, P. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des. 14, 487–494 (2000).
Article  CAS  PubMed  Google Scholar 
Globus, A., Lawton, J. & Wipke, W. T. Automatic Molecular design using evolutionary algorithms. Nanotechnology 10, 290–299 (1999).
Article  Google Scholar 
Brown, N., McKay, B., Gilardoni, F. & Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 44, 1079–1087 (2004).
Article  CAS  PubMed  Google Scholar 
Lipinski, C. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 23, 3–25 (1997).
Article  CAS  Google Scholar 
Teague, S. J. et al. The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed. Engl. 38, 3743–3747 (1999).
Article  CAS  PubMed  Google Scholar 
Aronov, A. M. Predictive in silico modeling for hERG channel blockers. Drug Discov. Today 10, 149–155 (2005).
Article  CAS  PubMed  Google Scholar 
Lewell, X. O., Budd, D. B., Watson, S. P. & Hann, M. M. RECAP – Retrosynthetic Combinatorial Analysis Procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J. Chem. Inf. Comput. Sci. 38, 511–522 (1998).
Article  CAS  PubMed  Google Scholar 
Vinkers, H. M. et al. SYNOPSIS: SYNthesize and OPtimize System in Silico. J. Med. Chem. 46, 2765–2773 (2003).
Article  CAS  PubMed  Google Scholar 
Honma, T. et al. Structure-based generation of a new class of potent Cdk4 inhibitors: new de novo design strategy and library design. J. Med. Chem. 44, 4615–4627 (2001).
Article  CAS  PubMed  Google Scholar 
Gillett, V., Johnson, P., Mata, P., Sike, S. & Williams, P. SPROUT: a program for structure generation. J. Comput. Aided Mol. Des. 7, 127–153 (1993).
Article  Google Scholar 
Gillet, V. et al. P. SPROUT: recent developments in the de novo design of molecules. J. Chem. Inf. Comput. Sci. 34, 207–217 (1994).
Article  CAS  PubMed  Google Scholar 
Mata, P. et al. SPROUT: 3D structure generation using templates. J. Chem. Inf. Comput. Sci. 35, 479–493 (1995).
Article  CAS  Google Scholar 
Ho, C. M. W. & Marshall, G. R. SPLICE: a program to assemble partial query solutions from three-dimensional database searches into novel ligands. J. Comput. Aided Mol. Des. 7, 623–647 (1993).
Article  CAS  Google Scholar 
Gelhaar, D. K. et al. De novo design of enzyme inhibitors by monte carlo ligand generation. J. Med. Chem. 38, 466–472 (1995).
Article  Google Scholar 
Pierce, A. C., Rao, G. & Bemis, G. W. BREED: generating novel inhibitors through hybridization of known ligands. application to CDK2, P38, and HIV protease. J. Med. Chem. 47, 2768–2775 (2004).
Article  CAS  PubMed  Google Scholar 
Todorov, N. P. & Dean, P. M. Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design. J. Comput. Aided. Mol. Des. 11, 175–192 (1997).
Article  CAS  PubMed  Google Scholar 
Todorov, N. P. & Dean, P. M. A branch-and-bound method for optimal atom-type assignment in de novo ligand design. J. Comput. Aided. Mol. Des. 12, 335–350 (1998).
Article  CAS  PubMed  Google Scholar 
Darwin, C. On the Origin of Species (Facsimile of the First Edition) (Harvard Univ. Press, Cambridge, Massachusetts, 1859/1975).
Google Scholar 
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
Article  CAS  Google Scholar 
Pegg, S. C. -H., Haresco, J. J. & Kuntz, I. D. A genetic algorithm for structure-based de novo design. J. Comput. Aided Mol. Des. 15, 911–933 (2001).
Article  CAS  PubMed  Google Scholar 
Schneider, G. & Böhm, H. -J. Virtual screening and fast automated docking methods. Drug Discov. Today 7, 64–70 (2002).
Article  CAS  PubMed  Google Scholar 
Hou, T. & Xu, X. Recent development and application of virtual screening in drug discovery: an overview. Curr. Pharm. Des. 10, 1011–1033 (2004).
Article  CAS  PubMed  Google Scholar 
Honma, T. Recent advances in de novo design strategy for practical lead identification. Med. Res. Rev. 23, 606–632 (2003).
Article  CAS  PubMed  Google Scholar 
Ji, H. et al. Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14α-demethylase of fungi. J. Med. Chem. 46, 474–485 (2003).
Article  CAS  PubMed  Google Scholar 
Perola, E., Walters, W. P. & Charifson, P. S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 56, 235–249 (2004).
Article  CAS  PubMed  Google Scholar 
Schuffenhauer, A. et al. Molecular diversity management strategies for building and enhancement of diverse and focused lead discovery compound screening collections. Comb. Chem. High Throughput Screen. 7, 771–781 (2004).
Article  CAS  PubMed  Google Scholar 
Honma, T. et al. A novel approach for the development of selective Cdk4 inhibitors: library design based on locations of Cdk4 specific amino acid residues. J. Med. Chem. 44, 4628–4640 (2001).
Article  CAS  PubMed  Google Scholar 
Rogers-Evans, M., Alanine, A. I., Bleicher, K. H., Kube, D. & Schneider, G. Identification of novel cannabinoid receptor ligands via evolutionary de novo design and rapid parallel synthesis. QSAR Comb. Sci. 23, 426–430 (2004).
Article  CAS  Google Scholar 
Böhm, H. -J., Banner, D. W. & Weber, L. Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J. Comput. Aided Mol. Des. 13, 51–56 (1999).
Article  PubMed  Google Scholar 
Obst, U., Banner, D. W., Weber, L. & Diederich, F. Molecular recognition at the thrombin active site: structure-based design and synthesis of potent and selective thrombin inhibitors and the X-ray crystal structures of two thrombin-inhibitor complexes. Chem. Biol. 4, 287–295 (1997).
Article  CAS  PubMed  Google Scholar 
Olsen, J. A. et al. A fluorine scan of thrombin inhibitors to map the fluorophilicity/fluorophobicity of an enzyme active site: evidence for C–F…C=O interactions. Angew. Chem. Int. Ed. Eng. 42, 2507–2511 (2003).
Article  CAS  Google Scholar 
Gribbon, P. & Sewing A. High-throughput drug discovery: what can we expect from HTS? Drug Discov. Today 10, 17–22 (2005).
Article  PubMed  Google Scholar 
Anderson, A. C. & Wright, D. L. The design and docking of virtual compound libraries to structures of drug targets. Curr. Comp. Aided Drug Des. 1, 103–127 (2005). An excellent overview of current developments in molecular docking and scoring and its relation to de novo design.
Article  CAS  Google Scholar 
Doweyko, A. M. 3D-QSAR illusions. J. Comp. Aided Mol. Des. 18, 587–596 (2004).
Article  CAS  Google Scholar 
Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).
Article  CAS  PubMed  Google Scholar 
Müller, G. in Chemogenomics in Drug Discovery (eds Kubinyi, H. & Müller, G.) 7–41 (Wiley-VCH, Weinheim, 2004).
Google Scholar 
Jenkins, J. L., Glick, M. & Davies, J. W. A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes. J. Med. Chem. 47, 6144–6159 (2004).
Article  CAS  PubMed  Google Scholar 
Bailey, D. & Brown, D. High-throughput chemistry and structure-based design: survival of the smartest. Drug Discov. Today 6, 57–59 (2001).
Article  CAS  PubMed  Google Scholar 
Verdonk, M. L. & Hartshorn, M. J. Structure-guided fragment screening for lead discovery. Curr. Opin. Drug Discov. Devel. 7, 404–410 (2004).
CAS  PubMed  Google Scholar 
Villar, H. O., Yan, J. & Hansen, M. R. Using NMR for ligand discovery and optimization. Curr. Opin. Chem. Biol. 8, 387–391 (2004).
Article  CAS  PubMed  Google Scholar 
Gillet, V. J., Khatib, W., Willett, P., Fleming, P. J. & Green, D. V. S. Combinatorial library design using a multiobjective genetic algorithm. J. Chem. Inf. Comput. Sci. 42, 375–385 (2002).
Article  CAS  PubMed  Google Scholar 
Fonseca, C. M. & Fleming, P. J. in Genetic Algorithms: Proceedings of the Fifth International Conference (ed. Forrest, S.) 416–423 (Morgan Kaufmann: San Mateo, CA, 1993).
Google Scholar 
Handschuh, S., Wagener, M. & Gasteiger, J. Superposition of three-dimensional chemical structures allowing for conformational flexibility by a hybrid method. J. Chem. Inf. Comput. Sci. 38, 220–232 (1998).
Article  CAS  PubMed  Google Scholar 
Agrafiotis, D. K. Multiobjective optimisation of combinatorial libraries. IBM J. Res. DeV. 45, 545–566 (2001).
Article  CAS  Google Scholar 
Wright, T., Gillet, V. J., Green, D. V. S. & Pickett, S. D. Optimizing the size and configuration of combinatorial libraries. J. Chem. Inf. Comput. Sci. 43, 381–390 (2003).
Article  CAS  PubMed  Google Scholar 
Babine, R. E. et al. Design, synthesis and X-ray crystallographic studies of novel FKBB-12 ligands. Bioorg. Med. Chem. Lett. 5, 1719–1724 (1995).
Article  CAS  Google Scholar 
Schindler, T. et al. Structural mechanism of STI-571 inhibition of Abelson tyrosine kinase. Science 289, 1938–1942 (2000).
Article  CAS  PubMed  Google Scholar 
Lewis, R. A. & Dean, P. M. Automated site-directed drug design: the concept of spacer skeletons for primary structure generation. Proc R Soc Lond B Biol Sci B 236, 125–140 (1989). Pioneering theoretical outline to tackle the problem of automated drug design from first principles.
PubMed  Google Scholar 
Böhm, H. -J. A novel computational tool for automated structure-based drug design. J. Mol. Recognit. 6, 131–137 (1993). Concise overview of the early developments of the program LUDI.
Article  PubMed  Google Scholar 
Böhm, H. -J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des. 8, 243–256 (1994).
Article  PubMed  Google Scholar 
Böhm, H. -J. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J. Comput. Aided Mol. Des. 12, 309–323 (1998).
Article  PubMed  Google Scholar 
Stultz, C. M. & Karplus, M. Dynamic ligand design and combinatorial optimization: designing inhibitors to endothiapepsin. Proteins 40, 258–289 (2000).
Article  CAS  PubMed  Google Scholar 
Westhead, D. R. et al. PRO LIGAND: an approach to de novo molecular design. 3. A genetic algorithm for structure refinement. J. Comput. Aided Mol. Des. 9, 139–148 (1995).
Article  CAS  PubMed  Google Scholar 
Frenkel, D. et al. PRO LIGAND: an approach de novo molecular design. 4. Application to the design of peptides. J. Comput. Aided Mol. Des. 9, 213–225 (1995).
Article  CAS  PubMed  Google Scholar 
Clark, D. E. & Murray, C. W. PRO LIGAND: an approach to de novo molecular design. 5. Tools for the Analysis of Generated Structures. J. Chem. Inf. Comput. Sci. 35, 914–923 (1995).
Article  CAS  Google Scholar 
Murray, C. W., Clark, D. E., Byrne, D. G. PRO LIGAND: an approach to de novo molecular design. 6. Flexible fitting in the design of peptides. J. Comput. Aided Mol. Des. 9, 381–395 (1995).
Article  CAS  PubMed  Google Scholar 
Grzybowski, B. A. et al. Combinatorial computational method gives new picomolar ligands for a known enzyme. Proc. Natl Acad. Sci. USA 99, 1270–1273 (2002). Design of a picomolar human carbonic anhydrase II inhibitor, the highest-affinity inhibitor to date, with the program SMoG.
Article  CAS  PubMed  PubMed Central  Google Scholar 
Nachbar, R. B. Molecular evolution: a hierarchical representation for chemical topology and its automated manipulation. Proc. 3rd Ann. Genetic Programming Conf. 246–253 (Univ. of Wisconsin, Madison, Wisconsin 1998).
Wermuth, C. G., Gannelin, C. R., Lindberg, P. and Mitscher, L. A. Glossary of terms used in medicinal chemistry. Pure Appl. Chem. 70, 1129–1143 (1998).
Article  CAS  Google Scholar 
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H. Kubinyi is thanked for helpful discussion and kind support. This work was supported by the Beilstein-Institut zur Förderung der Chemischen Wissenschaften, Frankfurt am Main. U.F. is thankful for a fellowship granted by Aventis Pharma Deutschland GmbH, a company of the Sanofi-Aventis group.
Johann Wolfgang Goethe-University, Institute of Organic Chemistry and Chemical Biology, Beilstein Endowed Chair for Cheminformatics, Marie-Curie-Str. 11 D-60439 Frankfurt am Main, Germany
Gisbert Schneider & Uli Fechner
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Correspondence to Gisbert Schneider.
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Glossary of terms used in medicinal chemistry
The design of bioactive compounds by incremental construction of a ligand model within a model of the receptor or enzyme active site, the structure of which is known from X-ray or NMR data¹⁰⁶.
The identification of isofunctional structures with different backbone architectures.
All information that is related to the ligand–receptor interaction — that is, the binding affinity of a ligand to the particular biological target.
A position in space that is not occupied by the receptor and in which a ligand atom favourably interacts with the receptor.
The ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response¹⁰⁶.
(QSAR). Mathematical relationships linking chemical structure and pharmacological activity in a quantitative manner for a series of compounds. Methods that can be used in QSAR include various regression and pattern-recognition techniques¹⁰⁶.
Non-deterministic polynomial-time hard (NP-hard) refers to a class of decision problems of which current knowledge provides no way to obtain or derive a solution time that is less than exponential in problem size.
Application of probabilistic rules grounded on knowledge of a particular problem domain to obtain an algorithm that performs ‘reasonably well’ in many cases, but without proof that it is always fast.
Essential drug properties apart from the binding affinity to a biological target — for example, absorption, distribution, metabolism, excretion and toxicity properties, or binding selectivity.
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