3D-QSAR illusions.
3D-QSAR is typically used to construct models (1) to predict activities, (2) to illustrate significant regions, and (3) to provide insight into possible interactions. To the contrary, examples are described herein which make it clear that the predictivity of such models remains elusive, that so-called significant regions are subject to the vagaries of alignment, and that the nature of possible interactions heavily depends on the eye of the beholder. Although great strides have been made in the imaginative use of 3D-descriptors, 3D-QSAR remains largely a retrospective analytical tool. The arbitrary nature of both the alignment paradigm and atom description lends itself to capricious models, which in turn can lead to distorted conclusions. Despite these illusionary pitfalls, predictions can be enhanced when the test set is bounded by the descriptor space represented in the training set. Interpretation of significant interaction regions becomes more meaningful when alignment is constrained by a binding site. Correlations obtained with a variety of atom descriptors suggest choosing useful ones, in particular, in guiding synthetic effort.
Doweyko
Arthur M
AM
Department of Macromolecular Structure, CADD, Pharmaceutical Research Institute, Bristol-Myers Squibb, Princeton, NJ 08543, USA. arthur.doweyko@bms.com
D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
J Med Chem. 1994 Nov 25;37(24):4130-46
7990113
J Chem Inf Comput Sci. 1999 May 25;39(3):11
27403845
J Med Chem. 2000 Mar 23;43(6):1109-22
10737743
J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):241-53
13677490
J Med Chem. 2002 Jan 3;45(1):41-53
11754578
J Med Chem. 2002 Oct 24;45(22):4858-67
12383011
J Comput Aided Mol Des. 1996 Jun;10(3):186-200
8808736
J Med Chem. 1964 Jul;7:395-9
14221113
J Comput Aided Mol Des. 1999 May;13(3):271-96
10216834
J Med Chem. 1998 Nov 19;41(24):4819-32
9822551
J Med Chem. 2000 Jan 27;43(2):167-76
10649972
J Med Chem. 1994 Jul 8;37(14):2206-15
8035428
J Med Chem. 2002 Feb 14;45(4):841-52
11831895
J Med Chem. 1994 Sep 2;37(18):2865-73
8071935
J Chem Inf Comput Sci. 2003 May-Jun;43(3):1020-7
12767161
J Chem Inf Comput Sci. 2001 Nov-Dec;41(6):1587-604
11749586
J Med Chem. 2002 Feb 14;45(4):973-83
11831910
J Med Chem. 2001 Apr 12;44(8):1151-7
11312915
J Comput Aided Mol Des. 1998 Jan;12(1):81-93
9570091
J Med Chem. 1997 Feb 14;40(4):427-36
9046332
J Am Chem Soc. 1988 Aug 1;110(18):5959-67
22148765
J Med Chem. 1999 Feb 25;42(4):573-83
10052964
J Comput Aided Mol Des. 2000 Aug;14 (6):545-57
10921771
J Comput Aided Mol Des. 1997 Jul;11(4):409-22
9334906
J Chem Inf Comput Sci. 1998 Sep-Oct;38(5):925-38
9770304
J Med Chem. 1988 Jul;31(7):1396-406
3290487
Bioorg Med Chem Lett. 2003 Apr 17;13(8):1403-8
12668000
Curr Pharm Des. 2003;9(20):1649-64
12871063
J Med Chem. 1996 May 24;39(11):2129-40
8667357
J Med Chem. 2002 Jun 20;45(13):2720-32
12061875
J Med Chem. 1998 Jul 2;41(14):2553-64
9651159
J Med Chem. 2002 Jan 17;45(2):292-303
11784134
J Med Chem. 1992 Jul 10;35(14):2543-50
1635056
J Mol Graph Model. 2002 Jan;20(4):269-76
11858635
Bioorg Med Chem Lett. 2003 May 19;13(10 ):1829-35
12729675
J Comput Aided Mol Des. 2000 Jan;14(1):1-21
10702922
J Comput Aided Mol Des. 1997 Mar;11(2):143-52
9089432
J Med Chem. 1996 Mar 15;39(6):1227-35
8632429
J Comput Aided Mol Des. 2000 Oct;14(7):647-57
11008886
J Comput Aided Mol Des. 1998 Mar;12(2):165-81
9690175
J Med Chem. 2002 Jan 17;45(2):333-43
11784138