Ligand-Based Combinatorial Design of Selective Purinergic Receptor (A_2A) Antagonists Using Self-Organizing Maps

Abstract

A virtual screening procedure based on a topological pharmacophore similarity metric and self-organizing maps (SOM) was developed and applied to optimizing combinatorial products functioning as P(1) purinergic receptor antagonists. The target was the human A(2A) receptor. A SOM was developed using a set of biologically tested molecules to establish a preliminary structure-activity relationship. A combinatorial library design was performed by projecting virtually assembled new molecules onto the SOM. A small focused library of 17 selected combinatorial products was synthesized and tested. On average, the designed structures yielded a 3-fold smaller binding constant ( approximately 33 vs approximately 100 nM) and 3.5-fold higher selectivity (50 vs 14) than the initial library. The most selective compound obtained revealed a 121-fold relative selectivity for A(2A) with K(i) (A(2A)) = 2.4 nM, and K(i) (A(1)) = 292 nM. This result demonstrates that it was possible to design a small, activity-enriched focused library with an improved property profile using the SOM virtual screening approach. The strategy might be particularly useful in projects in which structure-based design cannot be applied because of a lack of receptor structure information, for example, in the many projects aiming at finding new GPCR modulators.

Self-organizing Map Analysis for Understanding Comprehensive Relationships between Formulation Variables, State of Water, and the Physical Stability of Pharmaceutical Emulsions

“…16) Applications of the SOM have been reported across the range of pharmaceutical and medical fields. [17][18][19] We also have been applying this promising technique for the formulation design of pharmaceuticals. 10,11,20,21) The typical structure of the SOM comprises one input layer The model emulsion (#9) was packed in tubes and then stored at 60°C for up to 12 d, then, at designated intervals, the creaming was monitored using MRI.…”

ChemSpaceShuttle: A tool for data mining in drug discovery by classification, projection, and 3D visualization

“…There are many successful applications of the SOM concept in chemistry including mapping of chemical space [27], projection of molecular surface properties [28,29], clustering and diversity analysis [30,31], and property prediction [32,33]. Therefore, the application of SOM was not selected as an example for the present paper (see refs.…”

Applications of Artificial Neural Networks in Chemical Problems

“…In general, the neighborhood of an output neuron is defined as square or hexagonal and this means that each neuron has 4 or 6 nearest neighbors, respectively [51][52][53]. Figure 4 exemplifies the output layers of a SOM model using square and hexagonal neurons for a combinatorial design of purinergic receptor antagonists [54] and cannabinoid compounds [30], respectively. Example of output layers of SOM models using square and hexagonal neurons for the combinatorial design of (a) purinergic receptor antagonists [54] and (b) cannabinoid compounds [30], respectively.…”