GSK-3 inhibitors are interesting candidates to develop Anti-Alzheimer compounds. GSK-3β are also interesting as Anti-parasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the GSK-3 synthesis. Linear Discriminant Analysis was used to fit the classification function and it predicted heterogeneous series of compounds such as paullones, indirubins, meridians, etc. This study thus provided a general evaluation of these types of molecules. Plasmodium falciparum, Leishmania, Trypanosomes, among others, are the causers of diseases such as Malaria, Leishmaniasis and African Trypanosomiasis that are nowadays the most serious parasitic health problems. The great number of deaths and the few drugs against these parasites make it necessary to search for new drugs. Some of these antiparasitic drugs are also GSK-3 inhibitors. GSK-3 inhibitors are serious candidates as drugs for Alzheimers disease and other degenerative disorders. In this work, we revised different computational studies for a very large and heterogeneous series of GSK-3Is. The methods bioinformatics, molecular docking and quantitative structure-activity relationship (QSAR) were applied to develop new GSK-3Is. First, we revised QSAR studies with conceptual parameters such as flexibility of rotation, probability of availability, etc. We then used the method of regression analysis and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we reviewed 3DQSAR, CoMFA, CoMSIA and Docking, and analyzed a new and alternative QSAR model with different compounds to find out the structural requirements for GSK-3 inhibitory activity.
Keywords: QSAR, alzheimer, docking, GSK-3, parasitic, fungi, Plasmodium falciparum, Trypanosomes, Leishmania, phosphorylate glycogen synthase
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