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115. Liu, G., Huth, J. R., Olejniczak, E. T., Mendoza, R., DeVries, P., Leitza, S., Reilly, E. B.,
Okasinski, G. F., Fesik, S. W., and von Geldern, T. W. Novel p-arylthio cinnamides as
antagonists of leukocyte function-associated antigen-1/intracellular adhesion molecule-1
interaction. 2. Mechanism of inhibition and structure-based improvement of pharmaceutical properties. J. Med. Chem. 2001, 44(8), 1202–1210.
116. Hajduk, P. J., Sheppard, G., Nettesheim, D. G., Olejniczak, E. T., Shuker, S. B., Meadows,
R. P., Steinman, D. H., Carrera, G. M., Jr., Marcotte, P. A., Severin, J., Walter, K., Smith,
H., Gubbins, E., Simmer, R., Holzman, T. F.,Morgan, D. W., Davidsen, S. K., Summers, J.
B., and Fesik, S. W. Discovery of potent nonpeptide inhibitors of stromelysin using SAR
by NMR. J. Am. Chem. Soc. 1997, 119(25), 5818–5827.
117. Hajduk, P. J., Shuker, S. B., Nettesheim, D. G., Craig, R., Augeri, D. J., Betebenner, D.,
Albert, D. H., Guo, Y., Meadows, R. P., Xu, L., Michaelides, M., Davidsen, S. K., and
Fesik, S. W. NMR-based modification of matrix metalloproteinase inhibitors with
improved bioavailability. J. Med. Chem. 2002, 45(26), 5628–5639.
118. Szczepankiewicz, B. G., Liu, G., Hajduk, P. J., Abad-Zapatero, C., Pei, Z., Xin, Z.,
Lubben, T. H., Trevillyan, J. M., Stashko, M. A., Ballaron, S. J., Liang, H., Huang, F.,
Hutchins, C. W., Fesik, S. W., and Jirousek, M. R. Discovery of a potent, selective protein
tyrosine phosphatase 1B inhibitor using a linked-fragment strategy. J. Am. Chem. Soc.
2003, 125(14), 4087–4096.
119. Liu, G., Szczepankiewicz, B. G., Pei, Z., Janowick, D. A., Xin, Z., Hajduk, P. J., AbadZapatero, C., Liang, H., Hutchins, C. W., Fesik, S. W., Ballaron, S. J., Stashko, M. A.,
Lubben, T., Mika, A. K., Zinker, B. A., Trevillyan, J. M., and Jirousek, M. R. Discovery
and structure–activity relationship of oxalylarylaminobenzoic acids as inhibitors of
protein tyrosine phosphatase 1B. J. Med. Chem. 2003, 46(11), 2093–2103.
120. Liu, G., Xin, Z., Liang, H., Abad-Zapatero, C., Hajduk, P. J., Janowick, D. A.,
Szczepankiewicz, B. G., Pei, Z., Hutchins, C. W., Ballaron, S. J., Stashko, M. A.,
Lubben, T. H., Berg, C. E., Rondinone, C. M., Trevillyan, J. M., and Jirousek, M. R.
Selective protein tyrosine phosphatase 1B inhibitors: targeting the second phosphotyrosine binding site with non-carboxylic acid-containing ligands. J. Med. Chem. 2003, 46
(16), 3437–3440.
121. Liu, G., Xin, Z., Pei, Z., Hajduk, P. J., Abad-Zapatero, C., Hutchins, C. W., Zhao, H.,
Lubben, T. H., Ballaron, S. J., Haasch, D. L., Kaszubska, W., Rondinone, C. M.,
Trevillyan, J. M., and Jirousek, M. R. Fragment screening and assembly: a highly efficient
approach to a selective and cell active protein tyrosine phosphatase 1B inhibitor. J. Med.
Chem. 2003, 46(20), 4232–4235.
122. Erlanson, D. A., McDowell, R. S., He, M. M., Randal, M., Simmons, R. L., Kung, J.,
Waight, A., and Hansen, S. K. Discovery of a new phosphotyrosine mimetic for PTP1B
using breakaway tethering. J. Am. Chem. Soc. 2003, 125(19), 5602–5603.
123. Murray, C. W., Callaghan, O., Chessari, G., Cleasby, A., Congreve, M., Frederickson, M.,
Hartshorn, M. J., McMenamin, R., Patel, S., and Wallis, N. Application of fragment
screening by X-raycrystallography to b-secretase. J. Med. Chem. 2007, 50(6), 1116–1123.
124. Congreve, M., Aharony, D., Albert, J., Callaghan, O., Campbell, J., Carr, R. A., Chessari,
G., Cowan, S., Edwards, P. D., Frederickson, M., McMenamin, R., Murray, C. W., Patel,
S., and Wallis, N. Application of fragment screening by X-ray crystallography to the
discovery of aminopyridines as inhibitors of b-secretase. J. Med. Chem. 2007, 50(6),
1124–1132.
REFERENCES 471
https://t.me/medicina_free
125. Geschwindner, S., Olsson, L.-L., Albert, J. S., Deinum, J., Edwards, P. D., de Beer, T., and
Folmer, R. H. A. Discovery of a novel warhead against b-secretase through fragmentbased lead generation. J. Med. Chem. 2007, 50(24), 5903–5911.
126. Edwards, P. D., Albert, J. S., Sylvester, M., Aharony, D., Andisik, D., Callaghan, O.,
Campbell, J. B., Carr, R. A., Chessari, G., Congreve, M., Frederickson, M., Folmer, R. H.
A., Geschwindner, S., Koether, G., Kolmodin, K., Krumrine, J., Mauger, R. C., Murray,C.
W., Olsson, L.-L., Patel, S., Spear, N., and Tian, G. Application of fragment-based lead
generation to the discovery of novel, cyclic amidine b-secretase inhibitors with nanomolar potency, cellular activity, and high ligand efficiency. J. Med. Chem. 2007, 50(24),
5912–5925.
127. Yang, W., Fucini, R. V., Fahr, B. T., Randal, M., Lind, K. E., Lam, M. B., Lu, W., Lu, Y.,
Cary, D. R., Romanowski, M. J., Colussi, D., Pietrak, B., Allison, T. J., Munshi, S. K.,
Penny, D. M., Pham, P.,Sun, J., Thomas, A. E., Wilkinson, J. M., Jacobs, J. W., McDowell,
R. S., and Ballinger, M. D. Fragment-based discovery of nonpeptidic BACE-1 inhibitors
using tethering. Biochemistry 2009, 48(21), 4488–4496.
128. Kuglstatter, A., Stahl, M., Peters, J. U., Huber, W., Stihle, M., Schlatter, D., Benz, J., Ruf,
A., Roth, D., Enderle, T.,and Hennig, M. Tyramine fragment binding to BACE-1. Bioorg.
Med. Chem. Lett. 2008, 18(4), 1304–1307.
129. Lange, G., Lesuisse, D., Deprez, P., Schoot, B., Loenze, P., Benard, D., Marquette, J.-P.,
Broto, P., Sarubbi, E., and Mandine, E. Requirements for specific binding of low affinity
inhibitor fragments to the SH2 domain of
pp60
Src are identical to those for high affinity
binding of full length inhibitors. J. Med. Chem. 2003, 46(24), 5184–5195.
130. Neumann, T., Junker, H.-D., Keil, O., Burkert, K., Ottleben, H., Gamer, J., Sekul, R.,
Deppe, H., Feurer, A., Tomandl, D., and Metz, G. Discovery of thrombin inhibitor
fragments from chemical microarray screening. Lett. Drug Des. Discov. 2005, 2(8),
590–594.
131. Bulat, S., Bosio, S., Papadopoulos, M. A., Cerezo-Galvez, S., Grabowski, E., Rosenbaum,
C., Matassa, V. G., Ott, I., Metz, G., Schamberger, J., Sekul, R., and Feurer, A. Design and
discovery of novel, potent pyrazinone-based thrombin inhibitors with a solubilizing
amino P
1–P2
-linker. Lett. Drug Des. Discov. 2006, 3(5), 289–292.
132. Howard, N., Abell, C., Blakemore, W., Chessari, G., Congreve, M., Howard, S., Jhoti, H.,
Murray, C. W., Seavers, L. C. A., and van Montfort, R. L. M. Application of fragment
screening and fragment linking to the discovery of novel thrombin inhibitors. J. Med.
Chem. 2006, 49(4), 1346–1355.
133. Hajduk, P. J., Boyd, S., Nettesheim, D., Nienaber, V., Severin, J., Smith, R., Davidson, D.,
Rockway, T., and Fesik, S. W. Identification of novel inhibitors of urokinase via NMRbased screening. J. Med. Chem. 2000, 43(21), 3862–3866.
134. Patterson, A. W., Wood, W. J. L., Hornsby, M., Lesley, S., Spraggon, G., and Ellman, J. A.
Identification of selective, nonpeptidic nitrile inhibitors of cathepsin S using the substrate
activity screening method. J. Med. Chem. 2006, 49(21), 6298–6307.
135. Inagaki, H., Tsuruoka, H., Hornsby, M., Lesley, S. A., Spraggon, G., and Ellman, J. A.
Characterization and optimization of selective, nonpeptidic inhibitors of cathepsin S with
an unprecedented binding mode. J. Med. Chem. 2007, 50(11), 2693–2699.
136. Allen, D. A., Pham, P., Choong, I. C., Fahr, B., Burdett, M. T., Lew, W., DeLano, W. L.,
Gordon, E. M., Lam, J. W., O’Brien, T., and Lee, D. Identification of potent and novel
small-molecule inhibitors of caspase-3. Bioorg. Med. Chem. Lett. 2003, 13(21),
3651–3655.
472
FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
https://t.me/medicina_free
137. (a) Schneider, G., and Fechner, U. Computer-based de novo design of drug-like
molecules. Nat. Rev. Drug Discov. 2005, 4(8), 649–663; (b) Honma, T. Recent advances
in de novo design strategy for practical lead identification. Med. Res. Rev. 2003, 23(5),
606–632; (c) B€ohm, H.-J. Current computational tools for de novo ligand design. Curr.
Opin. Biotechnol. 1996, 7(4), 433–436; (d) Bohacek, R. S. and McMartin, C. Modern
computational chemistry and drug discovery: structure generating programs. Curr. Opin.
Chem. Biol. 1997, 1(2), 157–161.
138. (a) Danziger, D. J. and 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. 1989, 236(1283), 101–113; (b) Danziger, D. J. and Dean, P.
M. Automated site-directed drug design: the prediction and observation of ligand point
positions at hydrogen-bonding regions on protein surfaces. Proc. R. Soc. Lond. B Biol. Sci.
1989, 236(1283), 115–124; (c) Lewis, R. A. and Dean, P.M. Automated site-directed drug
design: the concept of spacer skeletons for primary structure generation. Proc. R. Soc.
Lond. B Biol. Sci. 1989, 236(1283) , 125–140; (d) Lewis, R. A. and Dean, P.M. Automated
site-directed drug design: the formation of molecular templates in primary structure
generation. Proc. R. Soc. Lond. B Biol. Sci. 1989, 236(1283), 141–162.
139. Gillet, V. J., Johnson, A. P., Mata, P., and Sike, S. Automated structure design in 3D.
Tetrahedron Comput. Methodol. 1990, 3(6), 681–696.
140. (a) Nishibata, Y. and Itai, A. Automatic creation of drug candidate structures based on
receptor structure. Starting point for artificial lead generation. Tetrahedron 1991, 47(43),
8985–8990; (b) Nishibata, Y. and Itai, A. Confirmation of usefulness of a structure
construction program based on three-dimensional receptor structure for rational lead
generation. J. Med. Chem. 1993, 36(20), 2921–2928.
141. (a) B€ohm, H.-J. The computer program LUDI: a new method for the de novo design of
enzyme inhibitors. J. Comput. Aided Mol. Des. 1992, 6(1), 61–78; (b) B€ohm, H.-J. LUDI:
rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput.-
Aided Mol. Des. 1992, 6(6), 593–606; (c) B€ohm, H.-J. Towards the automatic design of
synthetically accessible protein ligands: peptides, amides and peptidomimetics.
J. Comput. Aided Mol. Des. 1996, 10(4), 265–272.
142. Rotstein, S. H. and Murcko, M. A. GenStar: a method for de novo drug design. J. Comput.
Aided Mol. Des.
1993, 7(1), 23–43.
143. Rotstein, S. H. and Murcko, M. A. GroupBuild: a fragment-based method for de novo
drug design. J. Med. Chem. 1993, 36(12), 1700–1710.
144. (a) Gillet, V., Johnson, A. P., Mata, P., Sike, S., and Williams, P. SPROUT: a program for
structure generation. J. Comput. Aided Mol. Des. 1993, 7(2) , 127–153; (b) Gillet, V. J.,
Newell, W., Mata, P., Myatt, G., Sike, S., Zsoldos, Z., and Johnson, A. P. SPROUT: recent
developments in the de novo design of molecules. J. Chem. Inf. Comput. Sci. 1994, 34(1),
207–217; (c) Mata, P., Gillet, V. J., Johnson, A. P., Lampreia, J., Myatt, G. J., Sike, S., and
Stebbings, A. L. SPROUT: 3D Structure Generation Using Templates. J. Chem. Inf.
Comput. Sci. 1995, 35(3), 479–493.
145. Bohacek, R. S. and 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. 1994, 116(13), 5560–5571.
146. (a) Clark, D. E., Frenkel, D., Levy, S. A., Li, J., Murray, C. W., Robson, B., Waszkowycz,
B., and Westhead, D. R. PRO-LIGAND: an approach to de novo molecular design. 1.
Application to the design of organic molecules. J. Comput. Aided Mol. Des. 1995, 9(1),
REFERENCES 473
https://t.me/medicina_free
13–32; (b) Waszkowycz, B., Clark, D. E., Frenkel, D., Li, J., Murray, C. W., Robson, B.,
and Westhead,D. R. 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. 1994, 37(23), 3994–4002; (c) Westhead, D. R., Clark, D. E., Frenkel, D.,
Li, J., Murray, C. W., Robson, B., and Waszkowycz, B. PRO-LIGAND: an approach to de
novo molecular design. 3. A genetic algorithm for structure refinement. J. Comput. Aided
Mol. Des. 1995, 9(2), 139–148; (d) Frenkel, D., Clark, D. E., Li, J., Murray,C. W., Robson,
B., Waszkowycz, B., and Westhead, D. R. PRO_LIGAND: an approach to de novo
molecular design. 4. Application to the design of peptides. J. Comput. Aided Mol. Des.
1995, 9(3), 213–225; (e) Clark, D. E. and 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. 1995, 35(5), 914–923; (f) Murray, C. W., Clark, D. E., and 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. 1995, 9(5), 381–395.
147. (a) DeWitte, R. S. and 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. 1996, 118(47), 11733–11744; (b) DeWitte, R. S., Ishchenko, A. V., and
Shakhnovich, E. I. SMoG: de novo design method based on simple, fast, and accurate free
energy estimates. 2. Case studies in molecular design. J. Am. Chem. Soc. 1997, 119(20),
4608–4617; (c) Ishchenko, A. V. and Shakhnovich, E. I. SMall Molecule Growth 2001
(SMoG2001): an improved knowledge-based scoring function for protein–ligand interactions. J. Med. Chem. 2002, 45(13), 2770–2780; (d) Grzybowski, B. A., Ishchenko, A.
V., Kim, C.-Y., Topalov, G., Chapman, R., Christianson, D. W., Whitesides, G. M., and
Shakhnovich, E. I. Combinatorial computational method gives new picomolar ligands for
a known enzyme. Proc. Natl. Acad. Sci. USA 2002, 99(3), 1270–1273; (e) Grzybowski, B.
A., Ishchenko, A. V., Shimada, J., and Shakhnovich, E. I. From knowledge-based
potentials to combinatorial lead design in silico. Acc. Chem. Res. 2002, 35(5), 261–269.
148. Luo, Z., Wang, R., and Lai, L. RASSE: a new method for structure-based drug design.
J. Chem. Inf. Comput. Sci. 1996, 36(6), 1187–1194.
149. (a) Murray, C. W., Clark, D. E., Auton, T. R., Firth, M. A., Li, J., Sykes, R. A.,
Waszkowycz, B., Westhead, D. R., and Young, S. C. PRO_SELECT: combining
structure-based drug design and combinatorial chemistry for rapid lead discovery. 1.
Technology. J. Comput. Aided Mol. Des. 1997, 11(2), 193–207; (b) Eldridge, M. D.,
Murray, C. W., Auton, T. R., Paolini, G. V., and 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. 1997, 11(5), 425–445;
(c) Murray, C. W., Auton, T. R., and Eldridge, M. D. Empirical scoring functions. II. The
testing of an empirical scoring function for the prediction of ligand-receptor binding
affinities and the use of Bayesian regression to improve the quality of the model.
J. Comput. Aided Mol. Des. 1998, 12(5), 503–519; (d) Liebeschuetz, J. W., Jones, S.
D., Morgan, P. J., Murray, C. W., Rimmer, A. D., Roscoe, J. M. E., Waszkowycz, B.,
Welsh, P. M., Wylie, W. A., Young,S. C., Martin, H., Mahler, J., Brady, L., and Wilkinson,
K. PROSELECT: combining structure-based drug design and array-based chemistry for
rapid lead discovery. 2. The development of a series of highly potent and selective factor
Xa inhibitors. J. Med. Chem. 2002, 45(6), 1221–1232.
150. Wang, R., Gao, Y., and Lai, L. LigBuilder: a multi-purpose program for structure-based
drug design. J. Mol. Model. 2000, 6(7–8), 498–516.
474
FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
https://t.me/medicina_free
151. Pierce, A. C., Rao, G., and Bemis, G. W. BREED: Generating novel inhibitors through
hybridization of known ligands. Application to CDK2, p38, and HIV protease. J. Med.
Chem. 2004, 47(11), 2768–2775.
152. Moon, J. B. and Howe, W. J. Computer design of bioactive molecules: a method for
receptor-based de novo ligand design. Proteins 1991, 11(4), 314–328.
153. LeapFrog, SYBYL version 8.1, Tripos, Inc., St. Louis, MO, Available at http://www.tripos
.com.
154. Pellegrini, E. and Field, M. J. Development and testing of a de novo drug-design
algorithm. J. Comput. Aided Mol. Des. 2003, 17(10), 621–641.
155. Jorgensen, W. L. Efficient drug lead discovery and optimization. Acc. Chem. Res. 2009,
42(6), 724–733.
156. Tschinke, V. and Cohen, N. C. The NEWLEAD program: a new method for the design of
candidate structures from pharmacophoric hypotheses. J. Med. Chem. 1993, 36(24),
3863–3870.
157. (a) Ho, C. M. W. and Marshall, G. R. Cavity search: an algorithm for the isolation and
display of cavity-like binding regions. J. Comput. Aided Mol. Des. 1990, 4(4), 337–354;
(b) Ho, C. M. W. and Marshall, G. R. SPLICE: a program to assemble partial query
solutions from three-dimensional database searches into novel ligands. J. Comput. Aided
Mol. Des. 1993, 7(6), 623–647; (c) Ho, C. M. W.and Marshall, G. R. DBMAKER: a set of
programs to generate three-dimensional databases based upon user-specified criteria.
J. Comput. Aided Mol. Des. 1995, 9(1), 65–86.
158. Eisen, M. B., Wiley, D. C., Karplus, M., and Hubbard, R. E. HOOK: a program for finding
novel molecular architectures that satisfy the chemical and steric requirements of
a macromolecule binding site. Proteins 1994, 19(3), 199–221.
159. Lauri, G. and Bartlett, P. A. CAVEAT: a program to facilitate the design of organic
molecules. J. Comput. Aided Mol. Des. 1994, 8(1), 51–66.
160. Aronov, A. M. and Bemis, G. W. A minimalist approach to fragment-based ligand design
using common rings and linkers: application to kinase inhibitors. Proteins 2004, 57(1),
36–50.
161. (a) Caflisch, A. Computational combinatorial ligand design: application to human
a-thrombin. J. Comput. Aided Mol. Des. 1996, 10(5), 372–396; (b) Majeux, N., Scarsi,
M., Apostolakis, J., Ehrhardt, C., and Caflisch, A. Exhaustive docking of molecular
fragments with electrostatic solvation. Proteins 1999, 37(1), 88–105; (c) Majeux, N.,
Scarsi, M., Tenette-Souaille, C., and Caflisch, A. Hydrophobicity maps and docking of
molecular fragments with solvation. Perspect. Drug Discov. Des. 2000, 20, 145–169;
(d) Majeux, N., Scarsi, M., and Caflisch, A. Efficient electrostatic solvation model for
protein-fragment docking. Proteins 2001, 42(2), 256–268.
162. Degen, J. and Rarey, M. FlexNovo: structure-based searching in large fragment spaces.
ChemMedChem
2006, 1(8), 854–868.
163. (a) Fechner, U. and Schneider, G. Flux (1): a virtual synthesis scheme for fragment-based
de novo design. J. Chem. Inf. Model. 2006, 46(2), 699–707; (b) Fechner,U. and Schneider,
G. Flux (2): comparison of molecular mutation and crossover operators for ligand-based
de novo design. J. Chem. Inf. Model. 2007, 47(2), 656–667; (c) Sch€uller, A., Suhartono,
M., Fechner, U., Tanrikulu, Y.,Breitung, S., Scheffer, U., G€obel, M. W., and Schneider, G.
The concept of template-based de novo design from drug-derived molecular fragments
and its application to TAR RNA. J. Comput. Aided Mol. Des. 2008, 22(2), 59–68.
REFERENCES 475
https://t.me/medicina_free
164. Dey, F. and Caflisch, A. Fragment-based de novo ligand design by multiobjective
evolutionary optimization. J. Chem. Inf. Model. 2008, 48(3), 679–690.
165. Lewis, R. A. Automated site-directed drug design: approaches to the formation of 3D
molecular graphs. J. Comput. Aided Mol. Des. 1990, 4(2), 205–210.
166. (a) Lewis, R. A., Roe, D. C., Huang, C., Ferrin, T. E., Langridge, R., and Kuntz, I. D.
Automated site-directed drug design using molecular lattices. J. Mol. Graph. 1992, 10(2),
66–78, 106; (b) Roe, D. C. and Kuntz, I. D. BUILDER v.2: improving the chemistry of a de
novo design strategy. J. Comput. Aided Mol. Des. 1995, 9(3), 269–282.
167. Gehlhaar, D. K., Moerder, K. E., Zichi, D., Sherman, C. J., Ogden, R. C., and Freer, S. T.
De novo design of enzyme inhibitors by Monte Carlo ligand generation. J. Med. Chem.
1995, 38(3), 466–472.
168. (a) Miranker, A. and Karplus, M. An automated method for dynamic ligand design.
Proteins 1995, 23(4), 472–490; (b) Stultz, C. M. and Karplus, M. Dynamic ligand design
and combinatorial optimization: designing inhibitors to endothiapepsin. Proteins 2000,
40(2), 258–289.
169. (a) Todorov, N. P. and Dean, P. M. Evaluation of a method for controlling molecular
scaffold diversity in de novo ligand design. J. Comput. Aided Mol. Des. 1997, 11(2),
175–192; (b) Todorov, N. P. and Dean, P. M. A branch-and-bound method for
optimal atom-type assignment in de novo ligand design. J. Comput. Aided Mol. Des.
1998, 12(4), 335–349; (c) Stahl, M., Todorov, N. P., James, T., Mauser, H., Boehm, H.-J,
and Dean, P. M. A validation study on the practical use of automated de novo design.
J. Comput. Aided Mol. Des. 2002, 16(7), 459–478.
170. Pegg, S. C.-H., Haresco, J. J., and Kuntz, I. D. A genetic algorithm for structure-based de
novo design. J. Comput. Aided Mol. Des. 2001, 15(10), 911–933.
171. Lawrence, M. C. and Davis, P. C. CLIX: a search algorithm for finding novel ligands
capable of binding proteins of known three-dimensional structure. Proteins 1992, 12(1),
31–41.
172. Glen, R. C. and Payne, A. W. R. A genetic algorithm for the automated generation of
molecules within constraints. J. Comput. Aided Mol. Des. 1995, 9(2), 181–202.
173. Pearlman, D. A. and Murcko, M. A. CONCEPTS: new dynamic algorithm for de novo
drug suggestion. J. Comput. Chem. 1993, 14(10), 1184–1193.
174. Pearlman, D. A. and Murcko, M. A. CONCERTS: dynamic connection of fragments as an
approach to de novo ligand design. J. Med. Chem.
1996, 39(8), 1651–1663.
175. (a) Liu, H., Duan, Z., Luo, Q., and Shi, Y. Structure-based ligand design by dynamically
assembling molecular building blocks at binding site. Proteins 1999, 36(4), 462–470;
(b) Zhu, J., Yu, H., Fan, H., Liu, H., and Shi Y. Design of new selective inhibitors of
cyclooxygenase-2 by dynamic assembly of molecular building blocks. J. Comput. Aided
Mol. Des. 2001, 15(5), 447–463.
176. Zhu, J., Fan, H., Liu, H., and Shi, Y. Structure-based ligand design for flexible proteins:
application of new F-DycoBlock. J. Comput. Aided Mol. Des. 2001, 15(11), 979–996.
177. Nachbar, R. B. Molecular evolution: automated manipulation of hierarchical chemical
topology and its application to average molecular structures. Genet. Program. Evolv.
Mach. 2000, 1, 57–94.
178. Globus, A., Lawton, J., and Wipke, T. Automatic molecular design using evolutionary
techniques. Nanotechnology 1999, 10(3), 290–299.
476
FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
https://t.me/medicina_free
179. (a) Schneider, G., Clement-Chomienne, O., Hilfiger, L., Schneider, P., Kirsch, S., B€ohm,
H.-J., and Neidhart, W.Virtual screening for bioactive molecules by evolutionary de novo
design. Angew. Chem. Int. Ed. Engl. 2000, 39(22), 4130–4133; (b) Schneider, G., Lee,
M.-L., Stahl, M., and Schneider, P. De novo design of molecular architectures by
evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des.
2000, 14(5), 487–494; (c) Alig, L., Alsenz, J., Andjelkovic, M., Bendels, S., Benardeau,
A., Bleicher, K., Bourson, A., David-Pierson, P., Guba, W., Hildbrand, S., Kube, D.,
L€ubbers, T., Mayweg, A. V., Narquizian, R., Neidhart, W., Nettekoven, M.,
Plancher, J.-M., Rocha, C., Rogers-Evans, M., R€over, S., Schneider, G., Taylor, S., and
Waldmeier, P. Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the
treatment of obesity. J. Med. Chem. 2008, 51(7), 2115–2127.
180. Brown, N., McKay, B., Gilardoni, F., and Gasteiger, J. A graph-based genetic algorithm
and its application to the multiobjective evolution of median molecules. J. Chem. Inf.
Comput. Sci. 2004, 44(3), 1079–1087.
181. (a) Douguet, D., Thoreau, E., and Grassy, G. A genetic algorithm for the automated
generation of small organic molecules: drug design using an evolutionary algorithm.
J. Comput. Aided Mol. Des. 2000, 14(5), 449–466; (b) Douguet, D., Munier-Lehmann, H.,
Labesse, G., and Pochet, S. LEA3D: a computer-aided ligand design for structure-based
drug design. J. Med. Chem. 2005, 48(7), 2457–2468.
182. Vinkers, H. M., de Jonge, M. R., Daeyaert, F. F. D., Heeres, J., Koymans, L. M. H.,
van Lenthe, J. H., Lewi, P. J., Timmerman, H., Van Aken, K., and Janssen, P. A.
SYNOPSIS: SYNthesize and OPtimize System in Silico. J. Med. Chem. 2003, 46(13),
2765–2773.
183. (a) Boda, K., Seidel, T., and Gasteiger, J. Structure and reaction based evaluation of
synthetic accessibility.J. Comput. Aided Mol. Des. 2007, 21(6), 311–325; (b) Baber, J. C.,
and Feher, M. Predicting synthetic accessibility: application in drug discovery and
development. Mini. Rev. Med. Chem. 2004, 4(6), 681–692.
184. (a) Jorgensen, W. L. The many roles of computation in drug discovery. Science 2004, 303
(5665), 1813–1818; (b) Shoichet, B. K. Virtual screening of chemical libraries. Nature
2004, 432(7019), 862–865.
185. Gillet, V. J., Myatt, G., Zsoldos, Z., and Johnson, A. P. SPOUT, HIPPO and CAESA: tools
for de novo structure generation and estimation of synthetic accessibility. Perspect. Drug
Discov. Des. 1995, 3, 34–50.
186. (a) Honma, T., Hayashi, K., Aoyama, T., Hashimoto, N., Machida, T., Fukasawa, K.,
Iwama, T., Ikeura, C., Ikuta, M., Suzuki-Takahashi, I., Iwasawa, Y., Hayama, T.,
Nishimura, S., and Morishima, H. Structure-based generation of a new class of potent
Cdk4 inhibitors: new de novo design strategy and library design. J. Med. Chem. 2001, 44
(26), 4615–4627; (b) Honma, T., Yoshizumi, T., Hashimoto, N., Hayashi, K., Kawanishi,
N., Fukasawa, K., Takaki, T., Ikeura, C., Ikuta, M., Suzuki-Takahashi, I., Hayama, T.,
Nishimura, S., and Morishima, H. A novel approach for the development of selective
Cdk4 inhibitors: library design based on locations of Cdk4 specific amino acid residues.
J. Med. Chem. 2001, 44(26), 4628–4640.
187. Lewell, X. Q., Judd, D. B., Watson, S. P., and 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. 1998, 38(3), 511–522.
REFERENCES 477
https://t.me/medicina_free
188. (a) Leach, A. R., Shoichet, B. K., and Peishoff, C. E. Prediction of protein–ligand
interactions. Docking and scoring: successes and gaps. J. Med. Chem. 2006, 49(20),
5851–5855; (b) Gohlke, H. and Klebe, G. Approaches to the description and prediction of
the binding affinity of small-molecule ligands to macromolecular receptors. Angew.
Chem. Int. Ed. Engl. 2002, 41(15), 2644–2676.
189. (a) Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T.,
Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D. A., and Cheatham, T.
E. III., Calculating structures and free energies of complex molecules: combining
molecular mechanics and continuum models. Acc. Chem. Res. 2000, 33(12), 889–
897; (b) Jorgensen, W. L. and Thomas, L. L. Perspective on free-energy perturbation
calculations for chemical equilibria. J. Chem. Theory Comput. 2008, 4(6), 869–876.
190. (a) Jorgensen, W. L. and Tirado-Rives, J. Molecular modeling of organic and biomolecular systems using BOSS and MCPRO. J. Comput. Chem. 2005, 26(16), 1689–1700;
(b) Clark, M., Guarnieri, F., Shkurko, I., and Wiseman, J. Grand canonical Monte Carlo
simulation of ligand–protein binding. J. Chem. Inf. Model. 2007, 46(1), 231–242.
191. (a) A˚qvist, J., Medina, C., and Samuelsson, J.-E. A new method for predicting binding
affinity in computer-aided drug design. Protein Eng. 1994, 7(3), 385–391; (b) Hansson T,
Marelius J, and A˚qvist J. Ligand binding affinity prediction by linear interaction energy
methods. J. Comput. Aided Mol. Des. 1998, 12(1), 27–35.
192. (a) Kuhn, B. and Kollman, P. A. Binding of a diverse set of ligands to avidin and
streptavidin: an accurate quantitative prediction of their relative affinities by
a combination of molecular mechanics and continuum solvent models. J. Med. Chem.
2000, 43(20), 3786–3791; (b) Pearlman, D. A. Evaluating the molecular mechanics
Poisson–Boltzmann surface area free energy method using a congeneric series of ligands
to p38 MAP kinase. J. Med. Chem. 2005, 48(24), 7796–7807; (c) Barreiro, G., Guimar~aes,
C. R. W.,Tubert-Brohman, I., Lyons, T. M., Tirado-Rives, J., and Jorgensen, W. L. Search
for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity,
molecular docking, and MM-GB/SA scoring. J. Chem. Inf. Model. 2007, 47(6),
2416–2428.
193. Marcou, G. and Rognan, D. Optimizing fragment and scaffold docking by use of
molecular interaction fingerprints. J. Chem. Inf. Model. 2007, 47(1), 195–207.
194. (a) Chen, Y. and Shoichet, B. K. Molecular docking and ligand specificity in fragmentbased inhibitor discovery. Nat. Chem. Biol. 2009, 5(5), 358–364; (b) Teotico, D. G.,
Babaoglu, K., Rocklin, G. J., Ferreira, R. S., Giannetti, A. M., and Shoichet, B. K.
Docking for fragment inhibitors of AmpC b-lactamase. Proc. Natl. Acad. Sci. USA 2009,
106(18), 7455–7460.
195. (a) B€ohm, H.-J., Flohr, A., and Stahl, M. Scaffold hopping. Drug Discov. Today Technol.
2004, 1(3), 217–224; (b) Brown, N. and Jacoby, E. On scaffolds and hopping in medicinal
chemistry. Mini. Rev. Med. Chem. 2006, 6(11), 1217–1229; (c) Mauser, H. and Guba, W.
Recent developments in de novo design and scaffold hopping. Curr. Opin. Drug Discov.
Devel. 2008, 11(3), 365–374; (d) Krueger, B. A., Dietrich, A., Baringhaus, K. H., and
Schneider, G. Scaffold-hopping potential of fragment-based de novo design: the
chances and limits of variation. Comb. Chem. High Throughput Screen. 2009. 12(4),
383–396.
196. (a) Schneider, G., Schneider, P., and Renner, S. Scaffold-hopping: how far can you jump?
QSAR Comb. Sci. 2006, 25(12), 1162–1171; (b) Tsuchida, K., Chaki, H., Takakura, T.,
Kotsubo, H., Tanaka, T., Aikawa, Y., Shiozawa, S., and Hirono, S. Discovery of
478
FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
https://t.me/medicina_free
nonpeptidic small-molecule AP-1 inhibitors: lead hopping based on a three-dimensional
pharmacophore model. J. Med. Chem. 2006, 49(1), 80–91.
197. (a) Takahashi, Y., Sukekawa, M., and Sasaki, S.-i. Automatic identification of molecular
similarity using reduced-graph representation of chemical structure. J. Chem. Inf.
Comput. Sci. 1992, 32(6), 639–643; (b) Gillet, V. J., Willett, P., and Bradshaw, J.
Similarity searching using reduced graphs. J. Chem. Inf. Comput. Sci. 2003, 43(2),
338–345; (c) Barker, E. J., Gardiner, E. J., Gillet, V. J., Kitts, P., and Morris, J. Further
development of reduced graphs for identifying bioactive compounds. J. Chem. Inf.
Comput. Sci. 2003, 43(2), 346–356.
198. Cramer, R. D. Leadhopping—and beyond. Expert Opin. Drug Discov.2006, 1(4), 311–321.
199. (a) Schneider, G., Neidhart, W., Giller, T., and Schmid, G. “Scaffold-Hopping” by
topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int.
Ed. Engl. 1999, 38(19), 2894–2896; (b) Renner, S., Noeske, T., Parsons, C. G., Schneider,
P., Weil, T., and Schneider, G. New allosteric modulators of metabotropic glutamate
receptor 5 (mGluR5) found by ligand-based virtual screening. ChemBioChem 2005, 6(4),
620–625; (c) Renner, S., Ludwig, V., Boden, O., Scheffer, U., G€obel, M., and Schneider,
G. New inhibitors of the Tat-TAR RNA interaction found with a “fuzzy” pharmacophore
model. ChemBioChem 2005, 6(6), 1119–1125; (d) Renner, S. and Schneider, G. Scaffoldhopping potential of ligand-based similarity concepts. ChemMedChem 2006, 1(2),
181–185; (e) Renner, S., Schwab, C. H., Gasteiger, J., and Schneider, G. Impact of
conformational flexibility on three-dimensional similarity searching using correlation
vectors. J. Chem. Inf. Model. 2006, 46(6), 2324–2332; (f) Franke, L., Schwarz, O.,
M€uller-Kuhrt, L., Hoernig, C., Fischer, L., George, S., Tanrikulu, Y., Schneider, P., Werz,
O., and Steinhilber, D., Schneider, G. Identification of natural-product-derived inhibitors
of 5-lipoxygenase activity by ligand-based virtual screening. J. Med. Chem. 2007, 50(11),
2640–2646.
200. Hofmann, B., Franke, L., Proschak, E., Tanrikulu, Y., Schneider, P., Steinhilber, D., and
Schneider, G. Scaffold-hopping cascade yields potent inhibitors of 5-lipoxygenase.
ChemMedChem 2008, 3(10), 1535–1538.
201. Jenkins, J. L., Glick, M., and Davies, J. W. A 3D similarity method for scaffold hopping
from known drugs or natural ligands to new chemotypes. J. Med. Chem. 2004, 47(25),
6144–6159.
202. Schuffenhauer,A., Floersheim, P., Acklin, P., and Jacoby, E. Similarity metrics for ligands
reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 2003, 43(2),
391–405.
203. Renner, S. and Schneider, G. Fuzzy pharmacophore models from molecular
alignments for correlation-vector-based virtual screening. J. Med. Chem. 2004, 47
(19), 4653–4664.
204. (a) Tanrikulu, Y., Nietert, M., Scheffer, U., Proschak, E., Grabowski, K., Schneider, P.,
Weidlich, M., Karas, M., G
€
obel, M., and Schneider, G. Scaffold hopping by “fuzzy”
pharmacophores and its application to RNA targets. ChemBioChem 2007 8(16),
1932–1936; (b) Tanrikulu, Y., Rau, O., Schwarz, O., Proschak, E., Siems, K., M€ullerKuhrt, L., Schubert-Zsilavecz, M., and Schneider, G. Structure-based pharmacophore
screeningfornatural-product-derivedPPARg agonists. ChemBioChem 2009, 10(1), 75–78.
205. Barker, E. J., Buttar, D., Cosgrove, D. A., Gardiner, E. J., Kitts, P., Willett, P., and Gillet, V.
J. Scaffold hopping using clique detection applied to reduced graphs. J. Chem. Inf. Model.
2006, 46(2), 503–511.
REFERENCES 479
https://t.me/medicina_free
206. Stiefl, N., Watson, I. A., Baumann, K., and Zaliani, A. ErG: 2D pharmacophore
descriptions for scaffold hopping. J. Chem. Inf. Model. 2006, 46(1), 208–220.
207. Bohl, M., Loeprecht, B., Wendt, B., Heritage, T., Richmond, N. J., and Willett, P.
Unsupervised 3D ring template searching as an ideas generator for scaffold hopping: use
of the LAMDA, RigFit, and field-based similarity search (FBSS) methods. J. Chem. Inf.
Model. 2006, 46(5), 1882–1890.
208. Wolohan,P. R. N., Akella, L. B., Dorfman, R. J., Nell, P. G., Mundt, S. M., and Clark, R. D.
Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry. J. Chem. Inf. Model. 2006, 46(3), 1188–1193.
209. Maass, P., Schulz-Gasch, T., Stahl, M., and Rarey, M. Recore: a fast and versatile method
for scaffold hopping based on small molecule crystal structure conformations. J. Chem.
Inf. Model. 2007, 47(2), 390–399.
210. Ahlstr€om, M. M., Ridderstr€om, M., Luthman, K., and Zamora, I. Virtual screening and
scaffold hopping based on GRID molecular interaction fields. J. Chem. Inf. Model. 2005,
45(5), 1313–1323.
211. Bender, A., Mussa, H. Y., Gill, G. S., and Glen, R. C. Molecular surface point
environments for virtual screening and the elucidation of binding patterns (MOLPRINT
3D). J. Med. Chem. 2004, 47(26), 6569–6583.
212. Baroni, M., Cruciani, G., Sciabola, S., Perruccio, F., and Mason, J. S. A common
reference framework for analyzing/comparing proteins and ligands. Fingerprints for
Ligands and Proteins (FLAP): theory and application. J. Chem. Inf. Model. 2007, 47(2),
279–294.
213. (a) Bergmann, R., Linusson, A., and Zamora, I. SHOP: scaffold HOPping by GRID-based
similarity searches. J. Med. Chem. 2007, 50(11), 2708–2717; (b) Fontaine, F., Cross, S.,
Plasencia, G., Pastor, M., and Zamora, I. SHOP: a method for structure-based fragment
and scaffold hopping. ChemMedChem 2009, 4(3), 427–439; (c) Bergmann, R., Liljefors,
T., Sørensen, M. D., and Zamora, I. SHOP: receptor-based scaffold HOPping by GRIDbased similarity searches. J. Chem. Inf. Model. 2009, 49(3), 658–669.
214. (a) Rush, T. S. III, Grant, J. A., Mosyak, L., and Nicholls, A. A shape-based 3-D scaffold
hopping method and its application to a bacterial protein–protein interaction. J. Med.
Chem. 2005, 48(5), 1489–1495; (b) Kirchmair, J., Ristic, S., Eder, K., Markt, P., Wolber,
G., Laggner, C., and Langer, T. Fast and efficient in silico 3D screening: toward maximum
computational efficiency of pharmacophore-based and shape-based approaches. J. Chem.
Inf. Model. 2007, 47(6), 2182–2196; (c) Oyarzabal, J., Howe, T., Alcazar, J., Andres, J. I.,
Alvarez, R. M., Dautzenberg, F., Iturrino, L., Martınez, S., and Van der Linden, I. Novel
approach for chemotype hopping based on annotated databases of chemically feasible
fragments and a prospective case study: new melanin concentrating hormone antagonists.
J. Med. Chem. 2009, 52(7) , 2076–2089.
215. (a) Cheeseright, T., Mackey, M., Rose, S., and Vinter, A. molecular field extrema as
descriptors of biological activity: definition and validation. J. Chem. Inf. Model. 2006, 46
(2), 665–676; (b) Cheeseright, T. J., Mackey, M. D., Melville, J. L., and Vinter, J. G.
FieldScreen: virtual screening using molecular fields. Application to the DUD data set.
J. Chem. Inf. Model. 2008, 48(11), 2108–2117.
216. (a) Kalindjian, S. B., Buck, I. M., Davies, J. M. R., Dunstone, D. J., Hudson, M. L., Low,C.
M. R., McDonald, I. M., Pether, M. J., Steel, K. I., Tozer, M. J., and Vinter, J. G. Nonpeptide cholecystokinin-B/gastrin receptor antagonists based on bicyclic, heteroaromatic
skeletons. J. Med. Chem. 1996, 39(9), 1806–1815; (b) Low,C. M. R., Buck, I. M., Cooke,
480
FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
https://t.me/medicina_free
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