Green banana (Musa ssp.) mixed pulp and peel flour: A new ingredient with interesting bioactive, nutritional, and technological properties for food applications

This study aimed to characterize and evaluate the in vitro bioactive properties of green banana pulp (GBPF), peel (GBPeF), and mixed pulp/peel flours M1 (90/10) and M2 (80/20). Lipid concentration was higher in GBPeF (7.53%), as were the levels of free and bound phenolics (577 and 653.1 mg GAE/100 g, respectively), whereas the resistant starch content was higher in GBPF (44.11%). Incorporating up to 20% GBPeF into the mixed flour had a minor effect on the starch pasting properties of GBPF. GBPeF featured rutin and trans-ferulic acid as the predominant free and bound phenolic compounds, respectively. GBPF presented different major free phenolics, though it had similar bound phenolics to GBPeF. Both M1 and M2 demonstrated a reduction in intracellular reactive oxygen species (ROS) generation. Consequently, this study validates the potential of green banana mixed flour, containing up to 20% GBPeF, for developing healthy foods and reducing post-harvest losses.

Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Overcoming class imbalance in drug discovery problems: Graph neural networks and balancing approaches

This research investigates the application of Graph Neural Networks (GNNs) to enhance the cost-effectiveness of drug development, addressing the limitations of cost and time. Class imbalances within classification datasets, such as the discrepancy between active and inactive compounds, give rise to difficulties that can be resolved through strategies like oversampling, undersampling, and manipulation of the loss function. A comparison is conducted between three distinct datasets using three different GNN architectures. This benchmarking research can steer future investigations and enhance the efficacy of GNNs in drug discovery and design. Three hundred models for each combination of architecture and dataset were trained using hyperparameter tuning techniques and evaluated using a range of metrics. Notably, the oversampling technique outperforms eight experiments, showcasing its potential. While balancing techniques boost imbalanced dataset models, their efficacy depends on dataset specifics and problem type. Although oversampling aids molecular graph datasets, more research is needed to optimize its usage and explore other class imbalance solutions.

Discovery of Lead 2-Thiazolylhydrazones with Broad-Spectrum and Potent Antifungal Activity

Opportunistic fungal infections represent a global health problem, mainly for immunocompromised individuals. New therapeutical options are needed since several fungal strains show resistance to clinically available antifungal agents. 2-Thiazolylhydrazones are well-known as potent compounds against Candida and Cryptococcus species. A scaffold-focused drug design using machine-learning models was established to optimize the 2-thiazolylhydrazone skeleton and obtain novel compounds with higher potency, better solubility in water, and enhanced absorption. Twenty-nine novel compounds were obtained and most showed low micromolar MIC values against different species of Candida and Cryptococcus spp., including Candida auris, an emerging multidrug-resistant yeast. Among the synthesized compounds, 2-thiazolylhydrazone 28 (MIC value ranging from 0.8 to 52.17 μM) was selected for further studies: cytotoxicity evaluation, permeability study in Caco-2 cell model, and in vivo efficacy against Cryptococcus neoformans in an invertebrate infection model. All results obtained indicate the great potential of 28 as a novel antifungal agent.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

Structure-based discovery of thiosemicarbazones as SARS-CoV-2 main protease inhibitors

Aim: Discovery of novel SARS-CoV-2 main protease (Mpro) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover Mpro inhibitors, which were subsequently evaluated in biochemical and cellular assays. Results: 91 virtual hits were selected for biochemical assays, and four were confirmed as reversible inhibitors of SARS CoV-2 Mpro with IC50 values of 0.4-3 μM. They were also shown to inhibit SARS-CoV-1 Mpro and human cathepsin L. Molecular dynamics simulations indicated the stability of the Mpro inhibitor complexes and the interaction of ligands at the subsites. Conclusion: This approach led to the discovery of novel thiosemicarbazones as potent SARS-CoV-2 Mpro inhibitors.

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity

Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r² for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.

Ligand Accessibility Insights to the Dengue Virus NS3-NS2B Protease Assessed by Long-Timescale Molecular Dynamics Simulations

Dengue is a tropical disease caused by the dengue virus (DENV), with an estimate of 300 million new cases every year. Due to the limited vaccine efficiency and absence of effective antiviral treatment, new drug candidates are urgently needed. DENV NS3-NS2B protease complex is essential for viral post-translational processing and maturation, and this enzyme has been extensively studied as a relevant drug target. Crystal structures often underestimate NS3-NS2B flexibility, whereas they can adopt different conformational states depending on the bound substrate. We conducted molecular dynamics simulations (∼30 μs) with a non- and covalently bound inhibitor to understand the conformational changes in the DENV-3 NS3-NS2B complex. Our results show that the open-closing movement of the protease exposes multiple druggable subpockets that can be investigated in later drug discovery efforts.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

Cytotoxic Activity of Triterpenoids from Cheiloclinium cognatum Branches against Chronic and Acute Leukemia Cell Lines

Cheiloclinium cognatum (Miers) A.C.Sm. is an endemic species of Brazilian Cerrado that belongs to Celastraceae family. The phytochemical study of C. cognatum branches led to the identification of ten triterpenoids (TPs), 3β-acyloxyurs-12-ene (1), friedelin (2), β-friedelinol (3), glut-5-en-3β-ol (4), α-amyrin (5), β-amyrin (6), β-sitosterol (7), canophyllol (8), 29-hydroxyfriedelan-3-one (9) and friedelane-3β,29-diol (10). TPs 4, 5 and 6 are described for the first Cheiloclinium genus and TPs 8 and 9 were isolated in expressive amounts. Their cytotoxic activities were evaluated against THP-1 and K562 leukemia cell lines. TPs 3 and 5 were the most active, exhibiting lower or similar IC₅₀ against both cell lines when compared to the controls. Their mechanisms of action were investigated suggesting an intrinsic mitochondrial pathway of apoptosis evidenced by up-regulation of BAK mRNA expression. Chemometric studies indicated that their activities may be related to their molecular size and shape as well as electronic interactions of C-3 hydroxy group with molecular targets.

The application of machine learning techniques to innovative antibacterial discovery and development

INTRODUCTION

After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds).

AREAS COVERED

In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years.

EXPERT OPINION

The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.

Ligand- and Structure-Based Approaches of Escherichia coli FabI Inhibition by Triclosan Derivatives: From Chemical Similarity to Protein Dynamics Influence

Enoyl‐acyl carrier protein reductase (FabI) is the limiting step to complete the elongation cycle in type II fatty acid synthase (FAS) systems and is a relevant target for antibacterial drugs. E. coli FabI has been employed as a model to develop new inhibitors against FAS, especially triclosan and diphenyl ether derivatives. Chemical similarity models (CSM) were used to understand which features were relevant for FabI inhibition. Exhaustive screening of different CSM parameter combinations featured chemical groups, such as the hydroxy group, as relevant to distinguish between active/decoy compounds. Those chemical features can interact with the catalytic Tyr156. Further molecular dynamics simulation of FabI revealed the ionization state as a relevant for ligand stability. Also, our models point the balance between potency and the occupancy of the hydrophobic pocket. This work discusses the strengths and weak points of each technique, highlighting the importance of complementarity among approaches to elucidate EcFabI inhibitor's binding mode and offers insights for future drug discovery.

Pharmacological and physicochemical profile of arylacetamides as tools against human cancers

Arylacetamides are widely used as synthetic intermediates to obtain medicinal substances. This work evaluated in vitro antiproliferative activity of ten 2-Chloro-N-arylacetamides on human normal and cancer cells and detailed in vivo toxicological and anticancer investigations. Initially, cytotoxic colorimetric assays were performed using tumor lines, peripheral blood mononuclear cells (PBMC) and erythrocytes. Compounds 2, 3 and 4 were tested for acute toxicity (50, 150 and 300 mg/kg) and for subacute antitumoral capacity in HCT-116 colon carcinoma-bearing xenograft mice for 15 days at 25 mg/kg/day. Most compounds revealed cytotoxic action on tumor lines and PBMC, but activity on human erythrocytes were not detected. Molecular dipole moment, lipophilicity and electronic constant of aryl substituents had effects upon in vitro antiproliferative capacity. More common in vivo acute behavioral signals with compounds 2, 3 and 4 were muscle relaxation, reduction of spontaneous locomotor activity and number of entries in closed arms and increased number of falls andtime spent in open arms, suggesting diazepam-like anxiolytic properties. Decrease of grabbing strength and overall activity were common, but palpebral ptosis and deaths occurred at 300 mg/kg only. Compounds 2 and 3 reduced colon carcinoma growth (21.2 and 27.5%, respectively, p < 0.05) without causing apparent signals of organ-specific toxicity after subacute exposure. The structural chemical simplicity of arylacetamides make them cost-effective alternatives and justifies further improvements to enhance activity, selectivity and the development of pharmaceutical formulations.

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure-activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q² and r²_pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.Communicated by Ramaswamy H. Sarma.

Targeting the Plasmodium falciparum plasmepsin V by ligand-based virtual screening

Malaria is a devastating disease depending only on chemotherapy as treatment. However, medication is losing efficacy, and therefore, there is an urgent need for the discovery of novel pharmaceutics. Recently, plasmepsin V, an aspartic protease anchored in the endoplasmaic reticulum, was demonstrated as responsible for the trafficking of parasite-derived proteins to the erythrocytic surface and further validated as a drug target. In this sense, ligand-based virtual screening has been applied to design inhibitors that target plasmepsin V of P. falciparum (PMV). After screening 5.5 million compounds, four novel plasmepsin inhibitors have been identified which were subsequently analyzed for the potency at the cellular level. Since PMV is membrane-anchored, the verification in vivo by using transgenic PMV overexpressing P. falciparum cells has been performed in order to evaluate drug efficacy. Two lead compounds, revealing IC₅₀ values were 44.2 and 19.1 μm, have been identified targeting plasmepsin V in vivo and do not significantly affect the cell viability of human cells up to 300 μm. We herein report the use of the consensus of individual virtual screening as a new technique to design new ligands, and we propose two new lead compounds as novel protease inhibitors to target malaria.

Synthesis, molecular modeling studies and evaluation of antifungal activity of a novel series of thiazole derivatives

In the search for new antifungal agents, a novel series of fifteen hydrazine-thiazole derivatives was synthesized and assayed in vitro against six clinically important Candida and Cryptococcus species and Paracoccidioides brasiliensis. Eight compounds showed promising antifungal activity with minimum inhibitory concentration (MIC) values ranging from 0.45 to 31.2 μM, some of them being equally or more active than the drug fluconazole and amphotericin B. Active compounds were additionally tested for toxicity against human embryonic kidney (HEK-293) cells and none of them exhibited significant cytotoxicity, indicating high selectivity. Molecular modeling studies results corroborated experimental SAR results, suggesting their use in the design of new antifungal agents.

MD simulations and multivariate studies for modeling the antileishmanial activity of peptides

Leishmaniasis, a protozoan-caused disease, requires alternative treatments with minimized side-effects and less prone to resistance development. Antimicrobial peptides represent a possible choice to be developed. We report on the prospection of structural parameters of 23 helical antimicrobial and leishmanicidal peptides as a tool for modeling and predicting the activity of new peptides. This investigation is based on molecular dynamic simulations (MD) in mimetic membrane environment, as most of these peptides share the feature of interacting with phospholipid bilayers. To overcome the lack of experimental data on peptides' structures, we started simulations from designed 100% α-helices. This procedure was validated through comparisons with NMR data and the determination of the structure of Decoralin-amide. From physicochemical features and MD results, descriptors were raised and statistically related to the minimum inhibitory concentration against Leishmania by the multivariate data analysis technique. This statistical procedure confirmed five descriptors combined by different loadings in five principal components. The leishmanicidal activity depends on peptides' charge, backbone solvation, volume, and solvent-accessible surface area. The generated model possesses good predictability (q²  = 0.715, r²  = 0.898) and is indicative for the most and the least active peptides. This is a novel theoretical path for structure-activity studies combining computational methods that identify and prioritize the promising peptide candidates.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Synthesis of two ‘heteroaromatic rings of the future’ for applications in medicinal chemistry

Synthetic protocols that provide access on multigram scale to unknown heteroaromatic ring systems of potential value for medicinal chemistry.

Convergent QSAR studies on a series of NK₃ receptor antagonists for schizophrenia treatment

The dopamine hypothesis states that decreased dopaminergic neurotransmission reduces schizophrenia symptoms. Neurokinin-3 receptor (NK3) antagonists reduce dopamine release and have shown positive effects in pre-clinical and clinical trials. We employed 2D and 3D-QSAR analysis on a series of 40 non-peptide NK3 antagonists. Multivariate statistical analysis, PCA and HCA, were performed to rational training/test set splitting and PLS regression was employed to construct all QSAR models. We constructed one highly predictive CoMFA model (q(2)= 0.810 and r(2)= 0.929) and acceptable HQSAR and CoMSIA models (HQSAR q(2)= 0.644 and r(2)= 0.910; CoMSIA q(2)= 0.691, r(2)= 0.911). The three different techniques provided convergent physicochemical results. All models indicate cyclopropane, piperidine and di-chloro-phenyl ring attached to cyclopropane ring and also the amide group attached to the piperidine ring could play an important role in ligand-receptor interactions. These findings may contribute to develop potential NK3 receptor antagonists for schizophrenia.

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.

Molecular features related to the binding mode of PPARδ agonists from QSAR and docking analyses

Diabetes affects approximately 4% of world's population and metabolic syndrome has been directly related to obesity. There is a class of nuclear receptors, peroxisome proliferator-activated receptors (PPARs), which controls the metabolism of carbohydrates and lipids. It has been considered an attractive target to treat diabetes and metabolic syndrome. Accordingly, the primary objective of this study was to employ molecular modelling techniques to understand the factors involved in PPARδ activation. The QSAR models obtained showed good internal and external consistency and presented good validation coefficients (QSAR: q(2) = 0.83, r(2) = 0.87; HQSAR: q(2) = 0.73, r(2) = 0.90; CoMFA: q(2) = 0.88, r(2) = 0.94). The selected properties and the contour maps described the possible interactions between the PPARδ receptor and its agonists. From these findings, it is possible to propose molecular modifications to design new compounds with improved biological properties.