A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase.
Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative structure-activity relationships. An efficient feature selection method was emphasized in such an approach, using the confirmative results of different routine and novel feature selection methods. The proposed methods generated quite consistent results ensuring the effectiveness of the selected structural features.
Asadabadi
Ebrahim Barzegari
EB
Department of Biophysics, Tarbiat Modares University, Tehran, Iran.
Abdolmaleki
Parviz
P
Barkooie
Seyyed Mohsen Hosseini
SM
Jahandideh
Samad
S
Rezaei
Mohammad Ali
MA
D000110:Acetylcholinesterase / Q000737:chemistry*; D000465:Algorithms; D000544:Alzheimer Disease / Q000188:drug therapy / Q000201:enzymology; D001665:Binding Sites; D001921:Brain / Q000187:drug effects / Q000201:enzymology; D020134:Catalytic Domain; D002800:Cholinesterase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D020650:Combinatorial Chemistry Techniques; D003198:Computer Simulation; D016208:Databases, Factual; D016002:Discriminant Analysis; D015195:Drug Design; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D021281:Quantitative Structure-Activity Relationship
D002800:Cholinesterase Inhibitors; D000110:Acetylcholinesterase