Improved electron-conformational method of pharmacophore identification and bioactivity prediction. Application to angiotensin converting enzyme inhibitors
The electron-conformational (EC) method of pharmacophore (Pha) identificati
on and bioactivity prediction, suggested earlier, is given here: two major
improvements. First, an atomic index of orbital and,charge controlled inter
action is introduced to better represent the ligand (substrate) in its inte
raction with the bioreceptor. Second, the multiconformational problem is co
nsidered in view of ligand-receptor binding[states, resulting in essential
simplification of the expression of bioactivity. The details of the improve
d EC method are demonstrated in application to the problem of angiotensin c
onverting enzyme (ACE) inhibitors. The Pha of the latter is-identified by s
eparation of the heavily populated conformations of the chosen 51 compounds
(the training set),-calculation of the electronic structure, construction
of their EC matrixes of congruity, and processing of the latter in comparis
on with the activities to reveal a common submatrix of all the active only
compounds that describes the Pha. The latter contains three oxygen atoms pl
us a fourth atom X = S, N, O at certain interatomic distances and with rest
ricted electronic parameters (within assumed tolerances), the position of t
he atom X being more changeable from one active compound to another. For qu
antitative prediction of the bioactivity, an expression is deduced which ta
kes into account the duly parametrized influence of auxiliary groups (AG) w
hich, being positioned outside the Pha, either diminish the activity (antip
harmacophore shielding) or enhance it. It:is shown that in case of many con
formations of the same compound only one of them, that of the lowest energy
which has the Pha, should be parametrized. The 15 parameters chosen to rep
resent the AG in case of ACE inhibitors are weighted by variational (adjust
able) :coefficients which are determined from a regression treatment of the
calculated versus known activities in the training set. Then the formulas
with known coefficients are used to validate the method by calculating the
bioactivity of other compounds not used in the training set. The prediction
of the activity proved to be more than 90% (within experimental error and
available compounds). qualitatively (yes, no) and about 60%-70% quantitativ
ely.