Alzheimer disease (ADa) is the most common form of senile dementia, and it is characterized pathologically by decreased brain mass. An important problem to inhibiting β-secretase, is to cross the blood-brain barrier (BBB) using drugs not derived from proteins and thus more efficient to design drugs to treat Alzheimers disease. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. In the present work, we firstly revised two servers like ChEMBL or PDB to obtain databases of β-secretase inhibitors. Next, we review previous works based on 2D-QSAR, 3DQSAR, CoMFA, CoMSIA and Docking techniques, which studied different compounds to find out the structural requirements. Last, we carried out new QSAR studies using Artificial Neural Network (ANN) method and the software Modes- Lab in order to understand the essential structural requirement for binding with receptor for β-secretase inhibitors.
Keywords: QSAR, CoMSIA, COMFA, docking, topological indices, β-secretase inhibitors, alzheimer's disease (AD), Romuald.Bellmann@i-med.ac.at, Lipinski Parameters,, Transport Protein, Glycerol Acetate ion, Glycerol, Protein transport hidrolase, triphloroethol, Lamarckian Genetic Algorithm
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