Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase.
Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k(i) values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, the previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.
Sadat Hayatshahi
Sayyed Hamed
SH
Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran, Iran.
Abdolmaleki
Parviz
P
Safarian
Shahrokh
S
Khajeh
Khosro
K
D000225:Adenine / Q000031:analogs & derivatives* / Q000737:chemistry; D000243:Adenosine Deaminase / Q000737:chemistry; D058892:Adenosine Deaminase Inhibitors; D001665:Binding Sites; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
D058892:Adenosine Deaminase Inhibitors; D004791:Enzyme Inhibitors; D000243:Adenosine Deaminase; D000225:Adenine