New QSAR combined strategy for the design of A1 adenosine receptor agonists.
Combined discriminant and regression analysis was carried out on a series of 167 A1 adenosine receptor agonists to identify the best linear and nonlinear models for the design of new compounds with a better biological profile. On the basis of the best linear discriminant analysis and both linear and nonlinear Multi Layer Perceptron neural networks regression, we have designed and synthesized 14 carbonucleoside analogues of adenosine. Their biological activities were predicted and experimentally measured to demonstrate the capability of our model to avoid the prediction of false positives. A good agreement was found between the calculated and observed biological activity.
González
Maykel Pérez
MP
Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, C.P. 54830 Villa Clara, Cuba. mpgonzalez76@yahoo.es
Besada
Pedro
P
González Moa
Maria José
MJ
Teijeira
Marta
M
Terán
Carmen
C
D000241:Adenosine / Q000031:analogs & derivatives*; D058907:Adenosine A1 Receptor Agonists; D000818:Animals; D016002:Discriminant Analysis; D015195:Drug Design; D006801:Humans; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D058907:Adenosine A1 Receptor Agonists; D000241:Adenosine