Isela García

QSAR, Docking, and CoMFA Studies of GSK3 Inhibitors

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 GSK3 (glycogen synthase kinase 3 inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of 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 3D-QSAR, CoMFA and CoMSIA with different compounds to find out the structural requirements for GSK-3 inhibitory activity.

QSAR and complex network study of the chiral HMGR inhibitor structural diversity.

Efficient drugs such as statins or mevinic acids are inhibitors of the rate-limiting enzyme of cholesterol biosynthesis, 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGR), an enzyme responsible for the double reduction of 3-hydroxy-3-methyl-glutaryl coenzyme A into mevalonic acid. These compounds promoted the synthesis and evaluation of new inhibitors for HMGR, named HMGRIs. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the HMGRI synthesis. There are two main problems of the reported QSAR models: the homogeneous series of the compounds and the chirality of many candidates. In this work, we propose for the first time a QSAR model for a very large and heterogeneous series of HMGRIs. The model is based on the Topological Indices (TIs) of molecular structures. Using the predictions of this model as input, we construct the first complex network that describes the drug-drug similarity relationships for more than 1600 experimentally non-explored chiral HMGRIs isomers. We also presented a reduced version of this network (Giant Component) that contains the most representative set of chiral HMGRI candidates. The work suggests a new mixed application in the QSAR study of relevant aspects of structural diversity by using chiral/non-chiral TIs, combined with complex networks.

QSAR & Complex Network Study of the HMGR Inhibitors Structural Diversity

Efficient drugs such as statins or mevinic acids are inhibitors of the rate-limiting enzyme of cholesterol biosynthesis, 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGR), an enzyme responsible for the double reduction of 3-hydroxy-3-methyl-glutaryl coenzyme A. These compounds promoted the synthesis and evaluation of new inhibitors for HMGR, named HMGRIs. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the HMGRI (3-hydroxy-3-methyl-glutaryl coenzyme A inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of HMGRIs. 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 3D QSAR, CoMFA and CoMSIA with different compounds to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity.

Review of Synthesis, Biological Assay, and QSAR Studies of HMGR Inhibitors

Effective as statin drugs or acids are inhibitors mevinic limiting enzyme in cholesterol biosynthesis, 3-hydroxy- 3-methyl-glutaryl coenzyme A-3-hydroxy-3-reductase (HMGR), an enzyme responsible for the reduction the double methyl-glutaryl coenzyme A. These compounds promoted the synthesis and evaluation of new inhibitors of HMGR, called HMGRIs. The high number of potential candidates need to create models of quantitative structure-activity relationship in order to guide the HMGRI (3-hydroxy-3-methyl-glutarylcoenzyme A inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of HMGRIs. First, we revised QSAR studies with conceptual parameters how flexibility of rotation, probability of availability, etc; Next, using method of regression analysis; and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we review 3D QSAR, CoMFA and CoMSIA with different compound to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity.

Using topological indices to predict anti-Alzheimer and anti-parasitic GSK-3 inhibitors by multi-target QSAR in silico screening.

Plasmodium falciparum, Leishmania, Trypanosomes, are the causers of diseases such as malaria, leishmaniasis and African trypanosomiasis that nowadays are the most serious parasitic health problems worldwide. The great number of deaths and the few drugs available against these parasites, make necessary the search for new drugs. Some of these antiparasitic drugs also are GSK-3 inhibitors. GSKI-3 are candidates to develop drugs for the treatment of Alzheimer’s disease. In this work topological descriptors for a large series of 3,370 active/non-active compounds were initially calculated with the ModesLab software. Linear Discriminant Analysis was used to fit the classification function and it predicts heterogeneous series of compounds like paullones, indirubins, meridians, etc. This study thus provided a general evaluation of these types of molecules.

N-(furan-2-ylmethyl)-N-methylprop-2-yn-1-amine (F2MPA): A Potential Cognitive Enhancer with MAO Inhibitor Properties

A considerable body of human and animal experimental evidence links monoaminergic systems and cognition. Monoamine oxidase inhibitors (MAOIs), being able to enhance monoaminergic transmission and having neuroprotective properties, might represent a promising therapeutic strategy in cognitive impairment in Alzheimers disease (AD) and other dementias.

Theoretical study of GSK-3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors

Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK−3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK−3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK−3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK−3α inhibitors against different molecules found in the literature.

Review of QSAR for DNA polymerase inhibitors and new models for heterogeneous series of compounds.

DNA polymerases are essential enzymes for DNA replication, repair and recombination. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the DNA polymerase inhibitors. In this work, we revised different computational studies for a very large and heterogeneous series of DNA polymerase inhibitors. First, we reviewed QSAR methods with different compounds to find out the structural requirements for DNA polymerase inhibitory activity. Last, we report a new LDA analysis with the different molecular descriptors calculated with DRAGON software.