De novo drug design.

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

[1]  R C Glen,et al.  Molecular recognition using a binary genetic search algorithm. , 1993, Journal of molecular graphics.

[2]  Matthias Rarey,et al.  Similarity searching in large combinatorial chemistry spaces , 2001, J. Comput. Aided Mol. Des..

[3]  Michael M. Hann,et al.  RECAP-Retrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry , 1998, J. Chem. Inf. Comput. Sci..

[4]  Irwin D. Kuntz,et al.  A genetic algorithm for structure-based de novo design , 2001, J. Comput. Aided Mol. Des..

[5]  Valerie J. Gillet,et al.  SPROUT: A program for structure generation , 1993, J. Comput. Aided Mol. Des..

[6]  Darrell E Hurt,et al.  Structure of Plasmodium falciparum dihydroorotate dehydrogenase with a bound inhibitor. , 2006, Acta crystallographica. Section D, Biological crystallography.

[7]  A. Globus,et al.  Automatic molecular design using evolutionary techniques , 1999 .

[8]  Darren V S Green,et al.  Virtual screening of virtual libraries. , 2003, Progress in medicinal chemistry.

[9]  G Klebe,et al.  Energetic and entropic factors determining binding affinity in protein-ligand complexes. , 1997, Journal of receptor and signal transduction research.

[10]  G. Labesse,et al.  LEA3D: a computer-aided ligand design for structure-based drug design. , 2005, Journal of medicinal chemistry.

[11]  M. Murcko,et al.  GroupBuild: a fragment-based method for de novo drug design. , 1993, Journal of medicinal chemistry.

[12]  N. Cohen,et al.  The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses. , 1993, Journal of medicinal chemistry.

[13]  Antonio Macchiarulo,et al.  Exploring the other side of biologically relevant chemical space: insights into carboxylic, sulfonic and phosphonic acid bioisosteric relationships. , 2007, Journal of molecular graphics & modelling.

[14]  Valerie J. Gillet,et al.  Automated structure design in 3D , 1990 .

[15]  David E. Clark,et al.  PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology , 1997, J. Comput. Aided Mol. Des..

[16]  David G. Lloyd,et al.  Unbiasing Scoring Functions: A New Normalization and Rescoring Strategy , 2007, J. Chem. Inf. Model..

[17]  Dragos Horvath,et al.  Neighborhood Behavior of in Silico Structural Spaces with Respect to in Vitro Activity Spaces-A Novel Understanding of the Molecular Similarity Principle in the Context of Multiple Receptor Binding Profiles , 2003, J. Chem. Inf. Comput. Sci..

[18]  Peter Ertl,et al.  Estimation of pKa for Druglike Compounds Using Semiempirical and Information-Based Descriptors , 2007, J. Chem. Inf. Model..

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

[20]  Thomas Lampe,et al.  Discovery of the novel antithrombotic agent 5-chloro-N-({(5S)-2-oxo-3- [4-(3-oxomorpholin-4-yl)phenyl]-1,3-oxazolidin-5-yl}methyl)thiophene- 2-carboxamide (BAY 59-7939): an oral, direct factor Xa inhibitor. , 2005, Journal of medicinal chemistry.

[21]  Amedeo Caflisch,et al.  Fragment-Based de Novo Ligand Design by Multiobjective Evolutionary Optimization , 2008, J. Chem. Inf. Model..

[22]  Hwangseo Park,et al.  Structure-based de novo design and biochemical evaluation of novel Cdc25 phosphatase inhibitors. , 2009, Bioorganic & medicinal chemistry letters.

[23]  Hans-Joachim Böhm,et al.  The computer program LUDI: A new method for the de novo design of enzyme inhibitors , 1992, J. Comput. Aided Mol. Des..

[24]  G Schneider,et al.  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. , 1994, Biophysical journal.

[25]  Samo Turk,et al.  Design and synthesis of new hydroxyethylamines as inhibitors of D-alanyl-D-lactate ligase (VanA) and D-alanyl-D-alanine ligase (DdlB). , 2009, Bioorganic & medicinal chemistry letters.

[26]  N. P. Todorov,et al.  De novo ligand design to an ensemble of protein structures , 2006, Proteins.

[27]  Philip M. Dean,et al.  A validation study on the practical use of automated de novo design , 2002, J. Comput. Aided Mol. Des..

[28]  Ramaswamy Nilakantan,et al.  CONFIRM: connecting fragments found in receptor molecules , 2008, J. Comput. Aided Mol. Des..

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

[30]  M. Lambert,et al.  Substituted 2-[(4-aminomethyl)phenoxy]-2-methylpropionic acid PPARalpha agonists. 1. Discovery of a novel series of potent HDLc raising agents. , 2007, Journal of medicinal chemistry.

[31]  A. Hopkins,et al.  Ligand efficiency: a useful metric for lead selection. , 2004, Drug discovery today.

[32]  Valerie J. Gillet,et al.  SPROUT, HIPPO and CAESA: Tools for de novo structure generation and estimation of synthetic accessibility , 1995 .

[33]  Gisbert Schneider,et al.  Flux (2): Comparison of Molecular Mutation and Crossover Operators for Ligand-Based de Novo Design , 2007, J. Chem. Inf. Model..

[34]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[35]  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..

[36]  Barry Robson,et al.  PRO_LIGAND: An approach to de novo molecular design. 4. Application to the design of peptides , 1995, J. Comput. Aided Mol. Des..

[37]  David A. Pearlman,et al.  CONCEPTS: New dynamic algorithm for de novo drug suggestion , 1993, J. Comput. Chem..

[38]  Chris M. W. Ho,et al.  SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands , 1993, J. Comput. Aided Mol. Des..

[39]  Dominique Douguet,et al.  A genetic algorithm for the automated generation of small organic molecules: Drug design using an evolutionary algorithm , 2000, J. Comput. Aided Mol. Des..

[40]  Colin W. G. Fishwick,et al.  Synthesis of de novo designed small-molecule inhibitors of bacterial RNA polymerase , 2008 .

[41]  Gisbert Schneider,et al.  Design of MHC I stabilizing peptides by agent-based exploration of sequence space. , 2007, Protein engineering, design & selection : PEDS.

[42]  H. M. Vinkers,et al.  SYNOPSIS: SYNthesize and OPtimize System in Silico. , 2003, Journal of medicinal chemistry.

[43]  Haiyan Liu,et al.  Design of new selective inhibitors of cyclooxygenase-2 by dynamic assembly of molecular building blocks , 2001, J. Comput. Aided Mol. Des..

[44]  Kirsch,et al.  Virtual Screening for Bioactive Molecules by Evolutionary De Novo Design Special thanks to Neil R. Taylor for his help in preparation of the manuscript. , 2000, Angewandte Chemie.

[45]  Włodzisław Duch,et al.  Artificial intelligence approaches for rational drug design and discovery. , 2007, Current pharmaceutical design.

[46]  Philip M. Dean,et al.  A branch-and-bound method for optimal atom-type assignment in de novo ligand design , 1998, J. Comput. Aided Mol. Des..

[47]  Robert C. Glen,et al.  A genetic algorithm for the automated generation of molecules within constraints , 1995, J. Comput. Aided Mol. Des..

[48]  G. Whitesides,et al.  Combinatorial computational method gives new picomolar ligands for a known enzyme , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[49]  Conrad C. Huang,et al.  Automated site-directed drug design using molecular lattices , 1992 .

[50]  Richard A. Lewis,et al.  Automated site-directed drug design: the concept of spacer skeletons for primary structure generation , 1989, Proceedings of the Royal Society of London. B. Biological Sciences.

[51]  Johann Gasteiger,et al.  Structure and reaction based evaluation of synthetic accessibility , 2007, J. Comput. Aided Mol. Des..

[52]  Julian Tirado-Rives,et al.  Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. , 2006, Bioorganic & medicinal chemistry letters.

[53]  Philip M. Dean,et al.  Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design , 1997, J. Comput. Aided Mol. Des..

[54]  Gisbert Schneider,et al.  Reaction-MQL: Line Notation for Functional Transformation , 2009, J. Chem. Inf. Model..

[55]  J. Bajorath,et al.  SAR index: quantifying the nature of structure-activity relationships. , 2007, Journal of medicinal chemistry.

[56]  Harald Mauser,et al.  Chemical Fragment Spaces for de novo Design , 2007, J. Chem. Inf. Model..

[57]  G. Schneider,et al.  From Molecular Shape to Potent Bioactive Agents II: Fragment‐Based de novo Design , 2009, ChemMedChem.

[58]  G. Desiraju,et al.  Strong and weak hydrogen bonds in the protein–ligand interface , 2007, Proteins.

[59]  Constantinos S. Pattichis,et al.  De Novo Drug Design Using Multiobjective Evolutionary Graphs , 2009, J. Chem. Inf. Model..

[60]  Johann Gasteiger,et al.  A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules , 2004, J. Chem. Inf. Model..

[61]  Gisbert Schneider,et al.  Shapelets: Possibilities and limitations of shape‐based virtual screening , 2008, J. Comput. Chem..

[62]  Denis Khachko,et al.  A very large diversity space of synthetically accessible compounds for use with drug design programs , 2005, J. Comput. Aided Mol. Des..

[63]  Alexander Hillisch,et al.  Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. , 2006, Drug discovery today.

[64]  M. Karplus,et al.  Functionality maps of binding sites: A multiple copy simultaneous search method , 1991, Proteins.

[65]  Gisbert Schneider,et al.  Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity , 2007, J. Chem. Inf. Model..

[66]  Barry Robson,et al.  PRO_LIGAND: An approach to de novo molecular design. 1. Application to the design of organic molecules , 1995, J. Comput. Aided Mol. Des..

[67]  A. Johnson,et al.  Molecular complexity analysis of de novo designed ligands. , 2006, Journal of medicinal chemistry.

[68]  Petra Schneider,et al.  De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks , 2000, J. Comput. Aided Mol. Des..

[69]  R. Cramer,et al.  Prospective identification of biologically active structures by topomer shape similarity searching. , 1999, Journal of medicinal chemistry.

[70]  Wolfgang Guba,et al.  Recent developments in de novo design and scaffold hopping. , 2008, Current opinion in drug discovery & development.

[71]  Richard A. Lewis,et al.  Automated site-directed drug design : the formation of molecular templates in primary structure generation , 1989, Proceedings of the Royal Society of London. B. Biological Sciences.

[72]  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..

[73]  Jan M. Kriegl,et al.  From Molecular Shape to Potent Bioactive Agents I: Bioisosteric Replacement of Molecular Fragments , 2009, ChemMedChem.

[74]  D C Richardson,et al.  The de novo design of protein structures. , 1989, Trends in biochemical sciences.

[75]  P M Dean,et al.  Automated site-directed drug design: a general algorithm for knowledge acquisition about hydrogen-bonding regions at protein surfaces , 1989, Proceedings of the Royal Society of London. B. Biological Sciences.

[76]  E. Shakhnovich,et al.  SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. , 2002, Journal of medicinal chemistry.

[77]  Valerie J. Gillet,et al.  Knowledge-Based Approach to de Novo Design Using Reaction Vectors , 2009, J. Chem. Inf. Model..

[78]  Philip M Dean,et al.  Scaffold hopping in de novo design. Ligand generation in the absence of receptor information. , 2004, Journal of medicinal chemistry.

[79]  Matthias Rarey,et al.  Feature trees: A new molecular similarity measure based on tree matching , 1998, J. Comput. Aided Mol. Des..

[80]  Michael M. Hann,et al.  RECAP — Retrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry. , 1998 .

[81]  David Lou,et al.  FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules Occupying Druglike Chemical Space , 2009, J. Chem. Inf. Model..

[82]  Ksenia Oguievetskaia,et al.  Computational Fragment-Based Approach at PDB Scale by Protein Local Similarity , 2009, J. Chem. Inf. Model..

[83]  D. E. Clark,et al.  PRO_LIGAND: an approach to de novo molecular design. 2. Design of novel molecules from molecular field analysis (MFA) models and pharmacophores. , 1994, Journal of medicinal chemistry.

[84]  Valerie J. Gillet,et al.  SPROUT: Recent developments in the de novo design of molecules , 1994, J. Chem. Inf. Comput. Sci..

[85]  Gisbert Schneider,et al.  Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.

[86]  Wolfgang Guba,et al.  Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. , 2008, Journal of medicinal chemistry.

[87]  Petra Schneider,et al.  Scaffold Hopping by “Fuzzy” Pharmacophores and its Application to RNA Targets , 2007, Chembiochem : a European journal of chemical biology.

[88]  Klaus Gubernator,et al.  Optimization of the Biological Activity of Combinatorial Compound Libraries by a Genetic Algorithm , 1995 .

[89]  Matthias Rarey,et al.  FlexNovo: Structure‐Based Searching in Large Fragment Spaces , 2006, ChemMedChem.

[90]  Gisbert Schneider,et al.  Flux (1): A Virtual Synthesis Scheme for Fragment-Based de Novo Design , 2006, J. Chem. Inf. Model..

[91]  Luhua Lai,et al.  RASSE: A New Method for Structure-Based Drug Design , 1996, J. Chem. Inf. Comput. Sci..

[92]  J. Clardy,et al.  Structures of human dihydroorotate dehydrogenase in complex with antiproliferative agents. , 2000, Structure.

[93]  Adriaan P. IJzerman,et al.  Designing active template molecules by combining computational de novo design and human chemist's expertise. , 2007 .

[94]  Robert B. Nachbar,et al.  Molecular Evolution: Automated Manipulation of Hierarchical Chemical Topology and Its Application to Average Molecular Structures , 2000, Genetic Programming and Evolvable Machines.

[95]  Gisbert Schneider,et al.  Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening , 2008, Nature Reviews Drug Discovery.

[96]  Mark A. Murcko,et al.  GenStar: A method for de novo drug design , 1993, J. Comput. Aided Mol. Des..

[97]  N. P. Todorov,et al.  Receptor flexibility in de novo ligand design and docking. , 2005, Journal of medicinal chemistry.

[98]  Peter J. Fleming,et al.  Combinatorial Library Design Using a Multiobjective Genetic Algorithm , 2002, J. Chem. Inf. Comput. Sci..

[99]  Valerie J Gillet,et al.  New directions in library design and analysis. , 2008, Current opinion in chemical biology.

[100]  Gisbert Schneider,et al.  The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA , 2008, J. Comput. Aided Mol. Des..

[101]  G. Schneider,et al.  Voyages to the (un)known: adaptive design of bioactive compounds. , 2009, Trends in biotechnology.

[102]  W. Guida,et al.  The art and practice of structure‐based drug design: A molecular modeling perspective , 1996, Medicinal research reviews.

[103]  Matthias Rarey,et al.  Recore: A Fast and Versatile Method for Scaffold Hopping Based on Small Molecule Crystal Structure Conformations , 2007, J. Chem. Inf. Model..

[104]  D C Richardson,et al.  Looking at proteins: representations, folding, packing, and design. Biophysical Society National Lecture, 1992. , 1992, Biophysical journal.

[105]  Regine Bohacek,et al.  Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth , 1994 .

[106]  Brett A Tounge,et al.  Ligand efficiency and fragment-based drug discovery. , 2009, Drug discovery today.

[107]  Hans-Joachim Böhm,et al.  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 , 1998, J. Comput. Aided Mol. Des..

[108]  Timo Heikkilae,et al.  The first de novo designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase. , 2006, Bioorganic & medicinal chemistry letters.

[109]  Eric Pellegrini,et al.  Development and testing of a de novo drug-design algorithm , 2003, J. Comput. Aided Mol. Des..

[110]  H. Böhm,et al.  A novel computational tool for automated structure‐based drug design , 1993, Journal of molecular recognition : JMR.

[111]  Holger Claussen,et al.  Searching Fragment Spaces with Feature Trees , 2009, J. Chem. Inf. Model..

[112]  N. Foloppe,et al.  Towards predictive ligand design with free-energy based computational methods? , 2006, Current medicinal chemistry.

[113]  G. Bemis,et al.  BREED: Generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease. , 2004, Journal of medicinal chemistry.

[114]  Gary B. Fogel,et al.  A Novel In Silico Approach to Drug Discovery via Computational Intelligence , 2009, J. Chem. Inf. Model..

[115]  M Karplus,et al.  HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site , 1994, Proteins.

[116]  Hans-Joachim Böhm,et al.  LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads , 1992, J. Comput. Aided Mol. Des..

[117]  L. Weber,et al.  Simulated molecular evolution in a full combinatorial library. , 2000, Chemistry & biology.

[118]  L. M. Lima,et al.  Bioisosterism: a useful strategy for molecular modification and drug design. , 2005, Current medicinal chemistry.

[119]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

[120]  David E. Clark,et al.  PRO_LIGAND: An approach to de novo molecular design. 6. Flexible fitting in the design of peptides , 1995, J. Comput. Aided Mol. Des..

[121]  Markus Hartenfeller,et al.  Concept of Combinatorial De Novo Design of Drug‐like Molecules by Particle Swarm Optimization , 2008, Chemical biology & drug design.

[122]  Bruce Tidor,et al.  Additivity in the analysis and design of HIV protease inhibitors. , 2009, Journal of medicinal chemistry.

[123]  M. Murcko,et al.  CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. , 1996, Journal of medicinal chemistry.

[124]  Valerie J. Gillet,et al.  SPROUT: 3D Structure Generation Using Templates , 1995, J. Chem. Inf. Comput. Sci..

[125]  Luhua Lai,et al.  LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design , 2000 .

[126]  Christian Lemmen,et al.  Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces. , 2008, Journal of medicinal chemistry.

[127]  Olivier Roche,et al.  A new class of histamine H3 receptor antagonists derived from ligand based design. , 2007, Bioorganic & medicinal chemistry letters.

[128]  Yang Liu,et al.  Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation , 2009, J. Chem. Inf. Model..

[129]  Rommie E. Amaro,et al.  AutoGrow: A Novel Algorithm for Protein Inhibitor Design , 2009, Chemical biology & drug design.

[130]  Hans-Joachim Böhm,et al.  A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.

[131]  H Liu,et al.  Structure‐based ligand design by dynamically assembling molecular building blocks at binding site , 1999, Proteins.

[132]  Barry Robson,et al.  PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure refinement , 1995, J. Comput. Aided Mol. Des..

[133]  Dan C. Fara,et al.  Lead-like, drug-like or “Pub-like”: how different are they? , 2007, J. Comput. Aided Mol. Des..

[134]  Britta Nisius,et al.  Fragment Shuffling: An Automated Workflow for Three-Dimensional Fragment-Based Ligand Design , 2009, J. Chem. Inf. Model..

[135]  David E. Clark,et al.  PRO_LIGAND: An Approach to de Novo Molecular Design, 5. Tools for the Analysis of Generated Structures , 1995, J. Chem. Inf. Comput. Sci..

[136]  Haiyan Liu,et al.  Structure-based ligand design for flexible proteins: Application of new F-DycoBlock , 2001, J. Comput. Aided Mol. Des..

[137]  Miklos Feher,et al.  The use of ligand-based de novo design for scaffold hopping and sidechain optimization: two case studies. , 2008, Bioorganic & medicinal chemistry.

[138]  Andrew J. S. Knox,et al.  On the Effects of Permuted Input on Conformational Sampling of Drug‐like Molecules: an Evaluation of Stochastic Proximity Embedding , 2007, Chemical biology & drug design.