X-Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists.

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X-ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2A AR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2A AR were experimentally determined and investigated through a cycle of ligand-FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X-ray crystallography of the A2A AR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2A AR, an emerging target in immuno-oncology.

Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays.

ChEMBL biological activities prediction for 1-5-bromofur-2-il-2-bromo-2-nitroethene (G1) is a difficult task for cytokine immunotoxicity. The current study presents experimental results for G1 interaction with mouse Th1/Th2 and pro-inflammatory cytokines using a cytometry bead array (CBA). In the in vitro test of CBA, the results show no significant differences between the mean values of the Th1/Th2 cytokines for the samples treated with G1 with respect to the negative control, but there are moderate differences for cytokine values between different periods (24/48 h). The experiments show no significant differences between the mean values of the pro-inflammatory cytokines for the samples treated with G1, regarding the negative control, except for the values of tumor necrosis factor (TNF) and Interleukin (IL6) between the group treated with G1 and the negative control at 48 h. Differences occur for these cytokines in the periods (24/48 h). The study confirmed that the antimicrobial G1 did not alter the Th1/Th2 cytokines concentration in vitro in different periods, but it can alter TNF and IL6. G1 promotes free radicals production and activates damage processes in macrophages culture. In order to predict all ChEMBL activities for drugs in other experimental conditions, a ChEMBL data set was constructed using 25 biological activities, 1366 assays, 2 assay types, 4 assay organisms, 2 organisms, and 12 cytokine targets. Molecular descriptors calculated with Rcpi and 15 machine learning methods were used to find the best model able to predict if a drug could be active or not against a specific cytokine, in specific experimental conditions. The best model is based on 120 selected molecular descriptors and a deep neural network with area under the curve of the receiver operating characteristic of 0.904 and accuracy of 0.832. This model predicted 1384 G1 biological activities against cytokines in all ChEMBL data set experimental conditions.

Highly efficient one-pot assembly of peptides by double chemoselective coupling.

This study describes a methodological advancement in solution-phase peptide synthesis via the development of a convenient and operational protocol to synthesize oligopeptides in a one-pot three-step cascade method, in which two peptide bonds are introduced chemoselectively. Tri- to hexapeptides were obtained in high global yields (80-95%) with virtually no epimerization as determined via HPLC. The methodology described herein represents a faster, easier and milder approach to the synthesis of peptides, and it operates at equimolar amounts. This protocol comprises the formation of secondary and tertiary amides and is compatible with Z, Boc and Fmoc N-protecting groups as well as the use of d/l and non-proteinogenic amino acids.

Novel l-prolyl-l-leucylglycinamide (PLG) tripeptidomimetics based on a 2-azanorbornane scaffold as positive allosteric modulators of the D2R.

An efficient and straightforward orthogonal methodology was successfully developed to achieve constrained l-prolyl-l-leucylglycinamide (PLG) analogues starting from two proline mimetics based on a 2-azanorbornane scaffold. A preliminary dopamine D2 receptor radiolabeled binding assay with [3H]-N-propylnorapomorphine shows that enantiopurity of PLG peptidomimetics based on 2-azanorbornane is a requirement to achieve statistically significant positive modulators of the D2 receptor. This is the first documented active peptidomimetic of PLG whose bioactivity is not correlated with the C-terminal carboxamide pharmacophore and which cannot adopt the hypothesized type II ß-turn conformation.

Structure-based design of new KSP-Eg5 inhibitors assisted by a targeted multicomponent reaction.

An integrated multidisciplinary approach that combined structure-based drug design, multicomponent reaction synthetic approaches and functional characterization in enzymatic and cell assays led to the discovery of new kinesin spindle protein (KSP) inhibitors with antiproliferative activity. A focused library of new benzimidazoles obtained by a Ugi+Boc removal/cyclization reaction sequence generated low-micromolar-range KSP inhibitors as promising anticancer prototypes. The design and functional studies of the new chemotypes were assessed by computational modeling and molecular biology techniques. The most active compounds-20 (IC50 =1.49 µM, EC50 =3.63 µM) and 22 (IC50 =1.37 µM, EC50 =6.90 µM)-were synthesized with high efficiency by taking advantage of the multicomponent reactions.

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates.

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Exploring structural determinants of neuroprotection bias on novel glypromate conjugates with bioactive amines.

Neurodegenerative disorders of the central nervous system (CNS) such as Alzheimer's and Parkinson's diseases, afflict millions globally, posing a significant public health challenge. Despite extensive research, a critical hurdle in effectively treating neurodegenerative diseases is the lack of neuroprotective drugs that can halt or reverse the underlying disease processes. In this work, we took advantage of the neuroprotective properties of the neuropeptide glycyl-l-prolyl-l-glutamic acid (Glypromate) for the development of new peptidomimetics using l-pipecolic acid as a proline surrogate and exploring their chemical conjugation with relevant active pharmaceutical ingredients (API) via a peptide bond. Together with prolyl-based Glypromate conjugates, a total of 36 conjugates were toxicologically and biologically evaluated. In this series, the results obtained showed that a constrained ring (l-proline) at the central position of the peptide motif accounts for enhanced toxicological profiles and biological effects using undifferentiated and differentiated human neuroblastoma SH-SY5Y cells. Additionally, it was shown that biased biological responses are API-dependent. Conjugation with (R)-1-aminoindane led to a 38-43% reduction of protein aggregation induced by Aß25-35 (10 µM), denoting a 3.2-3.6-fold improvement in comparison with the parent neuropeptide, with no significative difference between functionalization at α and γ-carboxyl ends. On the other hand, the best-performing neuroprotective conjugate against the toxicity elicited by 6-hydroxydopamine (6-OHDA, 125 µM) was obtained by conjugation with memantine at the α-carboxyl end, resulting in a 2.3-fold improvement of the neuroprotection capacity in comparison with Glypromate neuropeptide. Altogether, the chemical strategy explored in this work shows that the neuroprotective capacity of Glypromate can be modified and fine-tuned, opening a new avenue for the development of biased neurotherapeutics for CNS-related disorders.

Exploring Biginelli-based scaffolds as A2B adenosine receptor antagonists: Unveiling novel structure-activity relationship trends, lead compounds, and potent colorectal anticancer agents.

Antagonists of the A2B adenosine receptor have recently emerged as targeted anticancer agents and immune checkpoint inhibitors within the realm of cancer immunotherapy. This study presents a comprehensive evaluation of novel Biginelli-assembled pyrimidine chemotypes, including mono-, bi-, and tricyclic derivatives, as A2BAR antagonists. We conducted a comprehensive examination of the adenosinergic profile (both binding and functional) of a large compound library consisting of 168 compounds. This approach unveiled original lead compounds and enabled the identification of novel structure-activity relationship (SAR) trends, which were supported by extensive computational studies, including quantum mechanical calculations and free energy perturbation (FEP) analysis. In total, 25 molecules showed attractive affinity (Ki < 100 nM) and outstanding selectivity for A2BAR. From these, five molecules corresponding to the new benzothiazole scaffold were below the Ki < 10 nM threshold, in addition to a novel dual A2A/A2B antagonist. The most potent compounds, and the dual antagonist, showed enantiospecific recognition in the A2BAR. Two A2BAR selective antagonists and the dual A2AAR/A2BAR antagonist reported in this study were assessed for their impact on colorectal cancer cell lines. The results revealed a significant and dose-dependent reduction in cell proliferation. Notably, the A2BAR antagonists exhibited remarkable specificity, as they did not impede the proliferation of non-tumoral cell lines. These findings support the efficacy and potential that A2BAR antagonists as valuable candidates for cancer therapy, but also that they can effectively complement strategies involving A2AAR antagonism in the context of immune checkpoint inhibition.

TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases.

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure-Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H2O2. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection=23.4% in assay (1) and protection=15.2% in assay (2); and for compound 7a with protection=46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.

Concise Overview of Glypromate Neuropeptide Research: From Chemistry to Pharmacological Applications in Neurosciences.

Neurodegenerative diseases of the central nervous system (CNS) pose a serious health concern worldwide, with a particular incidence in developed countries as a result of life expectancy increase and the absence of restorative treatments. Presently, treatments for these neurological conditions are focused on managing the symptoms and/or slowing down their progression. As so, the research on novel neuroprotective drugs is of high interest. Glypromate (glycyl-l-prolyl-l-glutamic acid, also known as GPE), an endogenous small peptide widespread in the brain, holds great promise to tackle neurodegenerative diseases such as Parkinson's, Alzheimer's, and Huntington's, s well as other CNS-related disorders like Rett and Down's syndromes. However, the limited pharmacokinetic properties of Glypromate hinder its clinical application. As such, intense research has been devoted to leveraging the pharmacokinetic profile of this neuropeptide. This review aims to offer an updated perspective on Glypromate research by exploring the vast array of chemical derivatizations of more than 100 analogs described in the literature over the past two decades. The collection and discussion of the most relevant structure-activity relationships will hopefully guide the discovery of new Glypromate-based neuroprotective drugs.

Pharmacological insights emerging from the characterization of a large collection of synthetic cannabinoid receptor agonists designer drugs.

Synthetic cannabinoid receptor agonists (SCRAs) constitute the largest and most defiant group of abuse designer drugs. These new psychoactive substances (NPS), developed as unregulated alternatives to cannabis, have potent cannabimimetic effects and their use is usually associated with episodes of psychosis, seizures, dependence, organ toxicity and death. Due to their ever-changing structure, very limited or nil structural, pharmacological, and toxicological information is available to the scientific community and the law enforcement offices. Here we report the synthesis and pharmacological evaluation (binding and functional) of the largest and most diverse collection of enantiopure SCRAs published to date. Our results revealed novel SCRAs that could be (or may currently be) used as illegal psychoactive substances. We also report, for the first time, the cannabimimetic data of 32 novel SCRAs containing an (R) configuration at the stereogenic center. The systematic pharmacological profiling of the library enabled the identification of emerging Structure-Activity Relationship (SAR) and Structure-Selectivity Relationship (SSR) trends, the detection of ligands exhibiting incipient cannabinoid receptor type 2 (CB2R) subtype selectivity and highlights the significant neurotoxicity of representative SCRAs on mouse primary neuronal cells. Several of the new emerging SCRAs are currently expected to have a rather limited potential for harm, as the evaluation of their pharmacological profiles revealed lower potencies and/or efficacies. Conceived as a resource to foster collaborative investigation of the physiological effects of SCRAs, the library obtained can contribute to addressing the challenge posed by recreational designer drugs.

Stapling Amantadine to Melanostatin Neuropeptide: Discovery of Potent Positive Allosteric Modulators of the D2 Receptors.

This work describes the synthesis and pharmacological and toxicological evaluation of melanostatin (MIF-1) bioconjugates with amantadine (Am) via a peptide linkage. The data from the functional assays at human dopamine D2 receptors (hD2R) showed that bioconjugates 1 (EC50 = 26.39 ± 3.37 nM) and 2 (EC50 = 17.82 ± 4.24 nM) promote a 3.3- and 4.9-fold increase of dopamine potency, respectively, at 0.01 nM, with no effect on the efficacy (Emax = 100%). In this assay, MIF-1 was only active at the highest concentration tested (EC50 = 23.64 ± 6.73 nM, at 1 nM). Cytotoxicity assays in differentiated SH-SY5Y cells showed that both MIF-1 (94.09 ± 5.75%, p < 0.05) and carbamate derivative 2 (89.73 ± 4.95%, p < 0.0001) exhibited mild but statistical significant toxicity (assessed through the MTT reduction assay) at 200 µM, while conjugate 1 was found nontoxic at this concentration.

Exploring the Effect of Halogenation in a Series of Potent and Selective A2B Adenosine Receptor Antagonists.

The modulation of the A2B adenosine receptor is a promising strategy in cancer (immuno) therapy, with A2BAR antagonists emerging as immune checkpoint inhibitors. Herein, we report a systematic assessment of the impact of (di- and mono-)halogenation at positions 7 and/or 8 on both A2BAR affinity and pharmacokinetic properties of a collection of A2BAR antagonists and its study with structure-based free energy perturbation simulations. Monohalogenation at position 8 produced potent A2BAR ligands irrespective of the nature of the halogen. In contrast, halogenation at position 7 and dihalogenation produced a halogen-size-dependent decay in affinity. Eight novel A2BAR ligands exhibited remarkable affinity (Ki < 10 nM), exquisite subtype selectivity, and enantioselective recognition, with some eutomers eliciting sub-nanomolar affinity. The pharmacokinetic profile of representative derivatives showed enhanced solubility and microsomal stability. Finally, two compounds showed the capacity of reversing the antiproliferative effect of adenosine in activated primary human peripheral blood mononuclear cells.

ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen.

Multiplexed biological assays provide multiple measurements of cellular parameters in the same test. In this work, we have trained and tested an Artificial Neural Network (ANN) model for the first time, in order to perform a multiplexing prediction of drugs effect on macrophage populations. In so doing, we have used the TOPS-MODE approach to calculate drug molecular descriptors and the software STATISTICA to seek different ANN models such as: Linear Neural Network (LNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN) and Multi-Layer Perceptrons (MLP). The best model found was the LNN, which correctly classified 8258 out of 9000 (Accuracy = 93.0%) multiplexing assay endpoints of 7903 drugs (including both training and test series). Each endpoint corresponds to one out of 1418 assays, 36 molecular or cellular targets, 46 standard type measures, in two possible organisms (human and mouse). Secondly, we have determined experimentally, for the first time, the values of EC(50) = 11.41 µg/mL and Cytotoxicity = 27.1% for the drug G1 over Balb/C mouse spleen macrophages using flow cytometry. In addition, we have used the LNN model to predict the G1 activity in 1265 multiplexing assays not measured experimentally (including 152 cytotoxicity assay endpoints). Both experimental and theoretical results point out a low macrophage cytotoxicity of G1. This work breaks new ground for the 'in silico' multiplexing screening of large libraries of compounds. The results obtained are very significant because they complement the immunotoxicology studies of this important anti-microbial/anti-parasite drug.

A2B adenosine receptor antagonists rescue lymphocyte activity in adenosine-producing patient-derived cancer models.

BACKGROUND: Adenosine is a metabolite that suppresses antitumor immune response of T and NK cells via extracellular binding to the two subtypes of adenosine-2 receptors, A2ARs. While blockade of the A2AARs subtype effectively rescues lymphocyte activity, with four A2AAR antagonists currently in anticancer clinical trials, less is known for the therapeutic potential of the other A2BAR blockade within cancer immunotherapy. Recent studies suggest the formation of A2AAR/A2BAR dimers in tissues that coexpress the two receptor subtypes, where the A2BAR plays a dominant role, suggesting it as a promising target for cancer immunotherapy. METHODS: We report the synthesis and functional evaluation of five potent A2BAR antagonists and a dual A2AAR/A2BAR antagonist. The compounds were designed using previous pharmacological data assisted by modeling studies. Synthesis was developed using multicomponent approaches. Flow cytometry was used to evaluate the phenotype of T and NK cells on A2BAR antagonist treatment. Functional activity of T and NK cells was tested in patient-derived tumor spheroid models. RESULTS: We provide data for six novel small molecules: five A2BAR selective antagonists and a dual A2AAR/A2BAR antagonist. The growth of patient-derived breast cancer spheroids is prevented when treated with A2BAR antagonists. To elucidate if this depends on increased lymphocyte activity, immune cells proliferation, and cytokine production, lymphocyte infiltration was evaluated and compared with the potent A2AAR antagonist AZD-4635. We find that A2BAR antagonists rescue T and NK cell proliferation, IFNγ and perforin production, and increase tumor infiltrating lymphocytes infiltration into tumor spheroids without altering the expression of adhesion molecules. CONCLUSIONS: Our results demonstrate that A2BAR is a promising target in immunotherapy, identifying ISAM-R56A as the most potent candidate for A2BAR blockade. Inhibition of A2BAR signaling restores T cell function and proliferation. Furthermore, A2BAR and dual A2AAR/A2BAR antagonists showed similar or better results than A2AAR antagonist AZD-4635 reinforcing the idea of dominant role of the A2BAR in the regulation of the immune system.

Exploring Non-orthosteric Interactions with a Series of Potent and Selective A3 Antagonists.

A library of potent and highly A3AR selective pyrimidine-based compounds was designed to explore non-orthosteric interactions within this receptor. Starting from a prototypical orthosteric A3AR antagonist (ISVY130), the structure-based design explored functionalized residues at the exocyclic amide L1 region and aimed to provide additional interactions outside the A3AR orthosteric site. The novel ligands were assembled through an efficient and succinct synthetic approach, resulting in compounds that retain the A3AR potent and selective profile while improving the solubility of the original scaffold. The experimentally demonstrated tolerability of the L1 region to structural functionalization was further assessed by molecular dynamics simulations, giving hints of the non-orthosteric interactions explored by these series. The results pave the way to explore newly functionalized A3AR ligands, including covalent drugs and molecular probes for diagnostic and delivery purposes.

Optimization of 2-Amino-4,6-diarylpyrimidine-5-carbonitriles as Potent and Selective A1 Antagonists.

We herein document a large collection of 108 2-amino-4,6-disubstituted-pyrimidine derivatives as potent, structurally simple, and highly selective A1AR ligands. The most attractive ligands were confirmed as antagonists of the canonical cyclic adenosine monophosphate pathway, and some pharmacokinetic parameters were preliminarilly evaluated. The library, built through a reliable and efficient three-component reaction, comprehensively explored the chemical space allowing the identification of the most prominent features of the structure-activity and structure-selectivity relationships around this scaffold. These included the influence on the selectivity profile of the aromatic residues at positions R4 and R6 of the pyrimidine core but most importantly the prominent role to the unprecedented A1AR selectivity profile exerted by the methyl group introduced at the exocyclic amino group. The structure-activity relationship trends on both A1 and A2AARs were conveniently interpreted with rigorous free energy perturbation simulations, which started from the receptor-driven docking model that guided the design of these series.

MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae.

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a public Web server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemented the model into the Web portal Bio-AIMS as an online server entitled MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (MIND-BEST), located at http://miaja.tic.udc.es/Bio-AIMS/MIND-BEST.php . This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally, we illustrated two practical uses of this server with two different experiments. In experiment 1, we report for the first time a MIND-BEST prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of eight rasagiline derivatives, promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, 3D structure modeling with LOMETS, and MIND-BEST prediction for different peptides as new protein of the found in the proteome of the bird parasite Trichomonas gallinae, which is promising for antiparasite drug targets discovery.

NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum.

There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately most QSAR models predict activity against only one protein target and/or have not been implemented in the form of public web server freely accessible online to the scientific community. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (sensitivity=90.12%) and 3083 out of 3408 nDTPs (specificity=90.46%), corresponding to training accuracy=90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (sensitivity=91.72%) and 1527 out of 1674 nDTP (specificity=91.22%) in validation series, corresponding to total accuracy=91.30% for validation series (predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at web portal Bio-AIMS in the form of an online server called: Non-Linear MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (NL MIND-BEST), which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php. This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally we illustrated two practical uses of this server with two different experiments. In experiment 1, we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF, and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and NL MIND-BEST prediction for different peptides a new protein of the found in the proteome of the human parasite Giardia lamblia, which is promising for anti-parasite drug-targets discovery.

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.