Abstract
The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined. The resulting library response to individual inhibitors was arranged in the array, preprocessed and transformed by the principal component analysis (PCA) and PLS procedures. A dominant absolute contribution to PC1 of the Glu attached to heptanoic gating acid and Phe bonded to the linker m-phenylenediamine/triazine scaffold was revealed by the PCA. The IVE-PLS procedure indicated the receptor systems with predominant Pro bonded to the linker and Glu, Gln, Cys and Val directly attached to the gating acid. The proposed comprehensive ligand-based and simplified structure-based methodology allows the in-depth study of the performance of peptide receptors against the tested set of compounds.
Highlights
Drug discovery is a complex process that is focused on the optimization of the molecular interactions between a drug molecule and a biomolecular target
→lead→seed→drug candidate potency and its ADMET-related properties to identify high-quality marketed drug molecules seems like searching ‘a needle in a haystack’ that lurks behind the complex property space that is delimitated by the complex nature of ligand-target interactions, which are ruled by inter-/intra-molecular phenomena [2,3]
comparative molecular surface analysis (CoMSA) modelling with satisfactory statistical parameters provides a spatial distribution of chemical groups/atoms potentially important for increasing/decreasing the activity profile of prospective anticancer agents
Summary
Drug discovery is a complex process that is focused on the optimization of the molecular interactions between a drug molecule and a biomolecular target. In absence of an empirically specified receptor structure, a valid estimation of the host binding geometry should be supplied using flexible sensors or molecular probes [9] It seems that a set of artificial receptors (AR) can compose a diagnostic platform capable to selectively interact with small chemical reagents under controlled conditions [10]. A natural question appears about preferable combinations of building blocks that comprise a ligand and/or a target structure Is it possible to tailor theoretically and/or empirically a protein cavity to enhance the target druggability in fragment-based domain design? We focused on the interpretation of noticeable variations in the potential selectiveness of individual inhibitor-receptor interactions; the combinatorial library of artificial dipeptide receptors (ADR) was designed, synthesized, empirically examined and arranged in the form of a probe matrix in the pseudo structure-based examination of ligand responses
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