First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines

Abstract

In the work described here, we developed the first multi-target quantitative structure-activity relationship (QSAR) model able to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns. GSK-3β inhibitors are interesting candidates for developing anti-Alzheimer compounds. GSK-3β are also of interest as anti-parasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The MARCH-INSIDE technique was used to quickly calculate total and local polarizability, n-octanol/water partition coefficients, refractivity, van der Waals area and electronegativity values to 4,508 active/non-active compounds as well as the average values of these indexes for active compounds in 42 different biological assays. Both the individual molecular descriptors and the average values for each test were used as input for a linear discriminant analysis (LDA). We discovered a classification function which used in training series correctly classifies 873 out of 1,218 GSK-3 cases of inhibitors (97.4%) and 2,140 out of 2,163 cases of non-active compounds (86.1%) in the 42 different tests. In addition, the model correctly classifies 285 out of 406 GSK-3 inhibitors (96.3%) and 710 out of 721 cases of non-active compounds (85.4%) in external validation series. The result is important because, for the first time, we can use a single equation to predict the results of heterogeneous series of organic compounds in 42 different experimental tests instead of developing, validating, and using 42 different QSAR models. Lastly, a double ordinate Cartesian plot of cross-validated residuals (first ordinate), standard residuals (second ordinate), and leverages (abscissa) defined the domain of applicability of the model as a squared area within ± 2 band for residuals and a leverage threshold of h = 0.0044.

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

“…47 Thus, in numerous fields of research in drug discovery, various works have reported the development of advanced chemoinformatic models inspired by the idea regarding the calculation of Box-Jenkins moving averages. [33][34][35][36][48][49][50][51][52][53][54][55][56][57][58] In the first step, the following equation is employed: In Eq. 3, n(c r ) represents the number of peptides assayed by considering the same element of the experimental condition (ontology) c r , which have also been annotated as positive.…”

<strong>Virtual screening of a cyclics imides to evaluate potential new multi-target agents against species of Leishmania</strong>

“…Additional non-MIFderived descriptors were generated to create a total of 128 descriptors. Volsurf descriptors have been previously used to predict antileishmanial activity of natural products on enzymes and predict activity of some molecules [17][18]. In a two-class classification, when a variable that is being investigated cannot be distinguished between the two groups (i.e., when there is no difference between the two distributions), the area under the ROC curve equals 0.5, which is to say that the ROC curve will coincide with the diagonal.…”

Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti- Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design

“…Many studies have been realized for search of new GSK-3β inhibitors by applying LBDD [39,[61][62][63][64][65][66][67][68][69][70]. The most promising work in this area is related with the first computational chemistry mt-QSAR model for anti-AD, antiparasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors [39]. In that study, the first mt-QSAR model able to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns has been developed.…”

“…Collectively, such methodologies have been able to provide the set up and detection of new drug candidates, optimizing time and resources. The use of QSAR methodologies have achieved considerable proportions, ranging from the analysis of homogeneous families of compounds [30][31][32], the screening of large heterogeneous database of molecules [33][34][35], to the simultaneous classification of different pharmacological profiles [36][37][38][39] and biological functions of proteins [40][41][42], including the use of complex networks theory (CNT) [43][44][45][46] and establishment of powerful web servers for drugs and target discovery [47][48][49][50]. The present work is focused on the role of the LBDD including QSAR methodologies pertaining to the design of new target inhibitors for anti-AD therapy.…”