Structure-based versus property-based approaches in the design of G-protein-coupled receptor-targeted libraries.
In this work, two alternative approaches to the design of small-molecule libraries targeted for several G-protein-coupled receptor (GPCR) classes were explored. The first approach relies on the selection of structural analogues of known active compounds using a substructural similarity method. The second approach, based on an artificial neural network classification procedure, searches for compounds that possess physicochemical properties typical of the GPCR-specific agents. As a reference base, 3365 GPCR-active agents belonging to nine different GPCR classes were used. General rules were developed which enabled us to assess possible areas where both approaches would be useful. The predictability of the neural network algorithm based on 14 physicochemical descriptors was found to exceed the predictability of the similarity-based approach. The structural diversity of high-scored subsets obtained with the neural network-based method exceeded the diversity obtained with the similarity-based approach. In addition, the descriptor distributions of the compounds selected by the neural network algorithm more closely approximate the corresponding distributions of the real, active compounds than did those selected using the alternative method.
D000465:Algorithms; D016208:Databases, Factual; D015195:Drug Design; D008024:Ligands; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D043562:Receptors, G-Protein-Coupled / Q000819:agonists* / Q000037:antagonists & inhibitors*
D008024:Ligands; D043562:Receptors, G-Protein-Coupled