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An optimization of the pyridylpiperazine series against Plasmodium falciparum has been performed, exploring a structure-activity relationship carried out on the toluyl fragment of hit 1, a compound with low micromolar activity against Plasmodium falciparum discovered by high-throughput screening. After confirming the crucial role played by this aryl fragment in the antiplasmodial activity, the replacement of the ortho-methyl substituent of 1 by halogenated ones led to an improvement for four analogs, either in terms of potency, expected pharmacokinetics profile, or both. Further introduction of endocyclic nitrogens in this fragment identified two more optimized compounds, 20 and 23, which are expected to be much more metabolically stable than 1. Additional assessment of the cytotoxicity, Ligand Lipophilic Efficiency, potency against the chloroquine-resistant Dd2 strain and in silico ADMET predictions revealed a satisfactory profile for most compounds, ultimately identifying the four optimized compounds 7, 9, 20 and 23 as promising compounds for further lead optimization of this series against Plasmodium falciparum., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The human protein Abelson kinase (Abl), a tyrosine kinase, plays a pivotal role in developing chronic myeloid leukemia (CML). Abl's involvement in various signaling pathways underscores its significance in regulating fundamental biological processes, including DNA damage responses, actin polymerization, and chromatin structural changes. The discovery of the Bcr-Abl oncoprotein, resulting from a chromosomal translocation in CML patients, revolutionized the understanding and treatment of the disease. The introduction of targeted therapies, starting with interferon-alpha and culminating in the development of tyrosine kinase inhibitors (TKIs) like imatinib, significantly improved patient outcomes. However, challenges such as drug resistance and side effects persist, indicating the necessity of research into novel therapeutic strategies. This review describes advancements in Abl kinase inhibitor development, emphasizing rational compound design from structural and regulatory information. Strategies, including bivalent inhibitors, PROTACs, and compounds targeting regulatory domains, promise to overcome resistance and minimize side effects. Additionally, leveraging the intricate structure and interactions of Bcr-Abl may provide insights into developing inhibitors for other kinases. Overall, this review highlights the importance of continued research into Abl kinase inhibition and its broader implications for therapeutic interventions targeting kinase-driven diseases. It provides valuable insights and strategies that may guide the development of next-generation therapies., (© 2024 Wiley‐VCH GmbH.)

The ( S )-norcoclaurine synthase from Thalictrum flavum ( Tf NCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give ( S )-norcoclaurine. Tf NCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions Tf NCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence Tf NCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the Tf NCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the Tf NCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the Tf NCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.

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 IC 50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC 50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC 50 = 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 DND i 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 .

This study describes the bioaccessibility in terms of total phenolic content (TPC) and antioxidant capacity before and after in vitro digestion from blackcurrant press cake extracts (BPC) and the bioactivity in cell culture, human erythrocytes as well as the in silico analysis. Chemical analysis of BPC presented an increase in TPC (270%) and anthocyanins (136%) after in vitro digestion, resulting in an improvement of antioxidant activity (DPPH 112%; FRAP: 153%). This behavior may be related to the highest activity of cyanidin-3-rutinoside, as confirmed by in silico analysis. The digested BPC did not exert cytotoxicity in cells and showed less antioxidant activity against the oxidative damage induced in endothelial cells and human erythrocytes compared to the non-digested extract. The results raise a question about the reliability we should place on results obtained only from crude samples, especially those that will be used to produce foods or nutraceuticals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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-resistant Staphylococcus 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.

The withanolides are naturally occurring steroidal lactones found mainly in plants of the Solanaceae family. The subtribe Withaninae includes species like Withania sominifera , which are a source of many bioactive withanolides. In this work, we selected and evaluate the ADMET-related properties of 91 withanolides found in species of the subtribe Withaninae computationally, to predict the relationship between their structures and their pharmacokinetic profiles. We also evaluated the interaction of these withanolides with known targets of Alzheimer's disease (AD) through molecular docking and molecular dynamics. Withanolides presented favorable pharmacokinetic properties, like high gastrointestinal absorption, lipophilicity (logP ≤ 5), good distribution and excretion parameters, and a favorable toxicity profile. The specie Withania aristata stood out as an interesting source of the promising withanolides classified as 5-ene with 16-ene or 17-ene. These withanolides presented a favourable pharmacokinetic profile and were also highlighted as the best candidates for inhibition of AD-related targets. Our results also suggest that withanolides are likely to act as cholinesterase inhibitors by interacting with the catalytic pocket in an energy favorable and stable way.Communicated by Ramaswamy H. Sarma.

Here, the antiviral activity of aminoadamantane derivatives were evaluated against SARS-CoV-2. The compounds exhibited low cytotoxicity to Vero, HEK293 and CALU-3 cells up to a concentration of 1,000 µM. The inhibitory concentration (IC 50 ) of aminoadamantane was 39.71 µM in Vero CCL-81 cells and the derivatives showed significantly lower IC 50 values, especially for compounds 3F4 (0.32 µM), 3F5 (0.44 µM) and 3E10 (1.28 µM). Additionally, derivatives 3F5 and 3E10 statistically reduced the fluorescence intensity of SARS-CoV-2 protein S from Vero cells at 10 µM. Transmission microscopy confirmed the antiviral activity of the compounds, which reduced cytopathic effects induced by the virus, such as vacuolization, cytoplasmic projections, and the presence of myelin figures derived from cellular activation in the face of infection. Additionally, it was possible to observe a reduction of viral particles adhered to the cell membrane and inside several viral factories, especially after treatment with 3F4. Moreover, although docking analysis showed favorable interactions in the catalytic site of Cathepsin L, the enzymatic activity of this enzyme was not inhibited significantly in vitro. The new derivatives displayed lower predicted toxicities than aminoadamantane, which was observed for either rat or mouse models. Lastly, in vivo antiviral assays of aminoadamantane derivatives in BALB/cJ mice after challenge with the mouse-adapted strain of SARS-CoV-2, corroborated the robust antiviral activity of 3F4 derivative, which was higher than aminoadamantane and its other derivatives. Therefore, aminoadamantane derivatives show potential broad-spectrum antiviral activity, which may contribute to COVID-19 treatment in the face of emerging and re-emerging SARS-CoV-2 variants of concern., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

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 (NS3 pro ) 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 NS3 pro 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 NS3 pro (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.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

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.

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., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Aim: Discovery of novel SARS-CoV-2 main protease (M pro ) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover M pro 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 M pro with IC 50 values of 0.4-3 μM. They were also shown to inhibit SARS-CoV-1 M pro and human cathepsin L. Molecular dynamics simulations indicated the stability of the M pro 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 M pro inhibitors.

Trypanosoma cruzi , the etiological agent of Chagas disease, relies on finely coordinated epigenetic regulation during the transition between hosts. Herein we targeted the silent information regulator 2 (Sir2) enzyme, a NAD + -dependent class III histone deacetylase, to interfere with the parasites' cell cycle. A combination of molecular modelling with on-target experimental validation was used to discover new inhibitors from commercially available compound libraries. We selected six inhibitors from the virtual screening, which were validated on the recombinant Sir2 enzyme. The most potent inhibitor (CDMS-01, IC 50 = 40 μM) was chosen as a potential lead compound.

In a previous work, the common gonadotrophic hormone α-subunit (ag-GTHα), the ag-FSH β- and ag-LH β-subunit cDNAs, were isolated and characterized by our research group from A. gigas pituitaries, while a preliminary synthesis of ag-FSH was also carried out in human embryonic kidney 293 (HEK293) cells. In the present work, the cDNA sequence encoding the ag-growth hormone (ag-GH) has also been isolated from the same giant Arapaimidae Amazonian fish. The ag-GH consists of 208 amino acids with a putative 23 amino acid signal peptide and a 185 amino acid mature peptide. The highest identity, based on the amino acid sequences, was found with the Elopiformes (82.0%), followed by Anguilliformes (79.7%) and Acipenseriformes (74.5%). The identity with the corresponding human GH (hGH) amino acid sequence is remarkable (44.8%), and the two disulfide bonds present in both sequences were perfectly conserved. Three-dimensional (3D) models of ag-GH, in comparison with hGH, were generated using the threading modeling method followed by molecular dynamics. Our simulations suggest that the two proteins have similar structural properties without major conformational changes under the simulated conditions, even though they are separated from each other by a >100 Myr evolutionary period (1 Myr = 1 million years). The sequence found will be used for the biotechnological synthesis of ag-GH while the ag-GH cDNA obtained will be utilized for preliminary Gene Therapy studies.

The Brazilian Compound Library (BraCoLi) is a novel open access and manually curated electronic library of compounds developed by Brazilian research groups to support further computer-aided drug design works, available on https://www.farmacia.ufmg.br/qf/downloads/ . Herein, the first version of the database is described comprising 1176 compounds. Also, the chemical diversity and drug-like profiles of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber's rules, alongside other drug-likeness properties. A comparison using principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBE DB ) regarding structural and physicochemical patterns. Furthermore, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity. Despite the similar distribution in the structural and physicochemical spaces, Tanimoto coefficient values indicated that compounds present in the BraCoLi are generally different from the two other databases, where they showed different kernel distributions and low similarity. These facts show an interesting innovative aspect, which is a desirable feature for novel drug design purposes., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The enzyme enoyl-ACP reductase (FabI) is the limiting step of the membrane's fatty acid biosynthesis in bacteria and a druggable target for novel antibacterial agents. The FabI active form is a homotetramer, which displays the highest affinity to inhibitors. Herein, molecular dynamics studies were carried out using the structure of FabI in complex with known inhibitors to investigate their effects on tetramerization. Our results suggest that multimerization is essential for the integrity of the catalytic site and that inhibitor binding enables the multimerization by stabilizing the substrate binding loop (SBL, L:195-200) coupled with changes in the H4/5 (QR interface). We also observed that AFN-1252 (naphtpyridinone derivative) promotes unique conformational changes affecting monomer-monomer interfaces. These changes are induced by AFN-1252 interaction with key residues in the binding sites (Ala95, Tyr146, and Tyr156). In addition, the analysis of water trajectories indicated that AFN-1252 complexes allow more water molecules to enter the binding site than triclosan and MUT056399 complexes. FabI-AFN-1252 simulations show accumulation of water molecules near the Tyr146/147 pocket, which can become a hotspot to the design of novel FabI inhibitors.

There is growing interest in exploring Digitalis cardenolides as potential antiviral agents. Hence, we herein investigated the influence of structural features and lipophilicity on the antiherpes activity of 65 natural and semisynthetic cardenolides assayed in vitro against HSV-1. The presence of an α,β-unsaturated lactone ring at C-17, a β-hydroxy group at C-14 and C-3β-OR substituents were considered essential requirements for this biological activity. Glycosides were more active than their genins, especially monoglycosides containing a rhamnose residue. The activity enhanced in derivatives bearing an aldehyde group at C-19 instead of a methyl group, whereas inserting a C-5β-OH improved the antiherpes effect significantly. The cardenolides lipophilicity was accessed by measuring experimentally their log P values (n-octanol-water partition coefficient) and disclosed a range of lipophilicity (log P 0.75±0.25) associated with the optimal antiherpes activity. In silico studies were carried out and resulted in the establishment of two predictive models potentially useful to identify and/or optimize novel antiherpes cardenolides. The effectiveness of the models was confirmed by retrospective analysis of the studied compounds. This is the first SAR study addressing the antiherpes activity of cardenolides. The developed computational models were able to predict the active cardenolides and their log P values., (© 2022 The Authors. Chemistry & Biodiversity published by Wiley-VHCA AG, Zurich, Switzerland.)

Introduction: Modern drug discovery is generally accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed., Areas Covered: One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future., Expert Opinion: Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

This study shows the changes in physicochemical and microbiological composition, and in the phenolic profile of black tea kombucha during fermentation. In addition, the antimalarial potential of the kombucha was evaluated. Ultra-performance liquid chromatography-mass spectrometry multiplex analysis (UPLC-MS E ) results revealed a 1.7 log 2 fold-change increase in phenolics with the fermentation time, with emphasis on the increase of phenolic acids (0.3 log 2 fold-change). Over time there was degradation of flavonoids such as nepetin, hesperidin and catechin 5-O-gallate, to the detriment of the increase in phenolic acids such as gallic acid and cinnamic acid. In addition, black tea kombucha presented antiplasmodic activity against the 3D7 (sensitive chloroquine) and W2 (resistant to chloroquine) strains. Therefore, important changes in the black tea kombucha phenolic profile take place during fermentation, which may help in the development of kombuchas with higher bioactive potential and contribute to a better understanding of the kombucha fermentation process., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Curcumin and its analogues exhibited anti-inflammatory activity in different experimental models. Recently, we synthesized (2E,3E)-3-buten-2-one-4-(4-hydroxy-3-methoxyphenyl)-2-(4-(4-methoxyphenyl)-2-thiazolyl)hydrazone (RI75), a curcumin analogue with a thiazolyl hydrazone moiety. In the present study, we investigated the effects induced by RI75 in different models of inflammation and pain in mice, as well as some underlying mechanisms. Pre-treatment with RI75 (40 mg/kg, intraperitoneal; i.p.) or curcumin (40 mg/kg, i.p.) reduced the mechanical allodynia and paw edema induced by intraplantar (i.pl) injection of carrageenan. RI75 antiallodynic activity was reduced by pre-treatment with naltrexone (5 and 10 mg/kg, i.p.) and cyproheptadine (10 mg/kg, i.p.), but not glibenclamide (20 and 40 mg/kg, i.p.). In a model of neuropathic pain, a single i.p. administration of RI75 (40 mg/kg) or curcumin (40 mg/kg) attenuated the ongoing mechanical allodynia induced by repeated administrations of paclitaxel. Pre-treatment with RI75 (40 mg/kg, i.p.) or curcumin (40 mg/kg, i.p.) also reduced tumor necrosis factor-α and interleukin-6 production and myeloperoxidase activity induced by carrageenan. The results of the present study demonstrate that RI75, a synthetic curcumin analogue, exhibits antiallodynic and antiedematogenic activities. Activation of opioidergic and serotonergic mechanisms and reduced production of inflammatory mediators and neutrophil recruitment may underlie RI75 activities., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design., (© 2021 Wiley-VCH GmbH.)

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 (r 2 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.

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.

Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR)., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This paper describes a comparative analysis of the physicochemical and structural properties of prodrugs and their corresponding drugs with regard to drug-likeness rules. The dataset used in this work was obtained from the DrugBank. Sixty-five pairs of prodrugs/drugs were retrieved and divided into the following categories: carrier-linked to increase hydrophilic character, carrier-linked to increase absorption, and bioprecursors. We compared the physicochemical properties related to drug-likeness between prodrugs and drugs. Our results show that prodrugs do not always follow Lipinski's Rule of 5, especially as we observed 15 prodrugs with more than 10 hydrogen bond acceptors and 18 with a molecular weight greater than 500 Da. This fact highlights the importance of extending Lipinski's rules to encompass other parameters as both strategies (filtering of drug-like chemical libraries and prodrug design) aim to improve the bioavailability of compounds. Therefore, critical reasoning is fundamental to determine whether a structure has drug-like properties or could be considered a potential orally active compound in the drug-design pipeline., (© 2021 Wiley-VCH GmbH.)

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19., (© 2021 The Author(s).)

RN104, named 2-[2-(cyclohexylmethylene)hydrazinyl)]-4-phenylthiazole, is a thiazolyl hydrazone derivative with promising antifungal activity. A HPLC-DAD method was carried out using C18 end-capped column (250 × 4.6 mm, 5 µm) and a mobile phase composed of water and acetonitrile (15:85 v/v) at a flow rate of 1.2 mL/min, injection volume 25 μL and DAD detection at 240 nm. The method showed to be selective, linear in the range of 20 to 240 µg/mL, precise, accurate and robust.Due to the low solubility of RN104 in water, the development of inclusion complexes using different cyclodextrins (β-CD, γ-CD and 2-HP-β-CD) was investigated, as well as the interaction mode between RN104 and cyclodextrins using molecular docking followed by semi-empirical calculations. Among tested cyclodextrins, the best results were obtained with 2-HP-β-CD, which promoted an 18-fold increase in RN104's aqueous solubility., (Copyright © 2020. Published by Elsevier B.V.)

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.

Aim: Cysteine proteases are important molecular targets involved in the replication, virulence and survival of parasitic organisms, including Trypanosoma and Leishmania species. Methodology & results: Analogs of the 7-chloro- N -[3-(morpholin-4-yl)propyl]quinolin-4-amine were synthesized and their inhibitory activity against the enzymes cruzain and rhodesain as well as against promastigotes forms of Leishmania species and epimastigotes forms of Trypanosoma cruzi were evaluated. Five compounds showed activity against both enzymes with half maximal inhibitory concentration (IC 50 ) values ranging from 23 to 123 μM. Among these, compounds 3 and 4 displayed leishmanicidal activity; compound 4  was the most promising with IC 50 values <10 μM and no cytotoxicity for uninfected cells. Conclusion: The results obtained indicate that cysteine proteases are likely to be the molecular target of compounds 3 and 4 .

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 ( q 2 and r 2 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.

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

The lack of predictivity of animal's models has increased the failure rate of drug candidates. Thus, the reversion of this scenario using preliminary in vitro assays and metabolism prediction can reduce the unnecessary use of animals, as well as predict toxic effects at preclinical and clinical stages. The present study aimed to evaluate safety of four biologically active molecules (RN104, RI78, ICH, PCH) with potential therapeutic applications synthesized in our laboratory. Initially, we used MTT cytotoxicity against A549, H9C2, HepG2, LLC-PK1 and NEURO-2 cell lines. RN104 showed the lowest cytotoxicity and further studies were conducted with it. The neutral red (NR) test was performed according to OECD-129 and then acute toxicity test (OECD-423). According to NR results we administered at 300 mg/kg on animals; however, no toxic effect was observed, while 2,000 mg/kg resulted in the death of one animal per group. After, metabolism prediction studies, performed using both ligand-based and structure-based, suggests three potential metabolites. In silico results suggested that potential metabolites could be fast eliminated and, then, this could be an explanation for lower observed toxicity in in vivo experiments. The results showed limitations of the NR as a predictor of the initial dose for the acute toxicity study, which may be related to metabolism. Therefore, the combination of theoretical and experimental studies is relevant to a general understanding of new molecule's toxicity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019. Published by Elsevier B.V.)

Aim: Antibiotic resistance is an alarming issue, as multidrug-resistant bacteria are growing worldwide, hence the decrease of therapeutic potential of available antibiotic arsenal. Among these bacteria, Staphylococcus aureus was pointed by the WHO in the pathogens list to be prioritized in drug development. Methods: We report the use of chemical similarity models for the virtual screening of new antibacterial with structural similarity to known inhibitors of FabI. The potential inhibitors were experimentally evaluated for antibacterial activity and membrane disrupting capabilities. Results & conclusion: These models led to the finding of four new compounds with antibacterial activity, one of which having antimicrobial activity already reported in the literature.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds., (Copyright © 2019 Lipinski, Maltarollo, Oliveira, da Silva and Honorio.)

Synthetic 1,3-bis(aryloxy)propan-2-amines have been shown in previous studies to possess several biological activities, such as antifungal and antiprotozoal. In the present study, we describe the antibacterial activity of new synthetic 1,3-bis(aryloxy)propan-2-amines against Gram-positive pathogens (Streptococcus pyogenes, Enterococcus faecalis and Staphylococcus aureus) including Methicillin-resistant S. aureus strains. Our compounds showed minimal inhibitory concentrations (MIC) in the range of 2.5-10 μg/ml (5.99-28.58 μM), against different bacterial strains. The minimal bactericidal concentrations found were similar to MIC, suggesting a bactericidal mechanism of action of these compounds. Furthermore, possible molecular targets were suggested by chemical similarity search followed by docking approaches. Our compounds are similar to known ligands targeting the cell division protein FtsZ, Quinolone resistance protein norA and the Enoyl-[acyl-carrier-protein] reductase FabI. Taken together, our data show that synthetic 1,3-bis(aryloxy)propan-2-amines are active against Gram-positive bacteria, including multidrug-resistant strains and can be a promising lead in the development of new antibacterial compounds for the treatment of these infections., (© 2019 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Trichomonas vaginalis is the causative agent of trichomoniasis, a highly prevalent sexually transmitted infection worldwide. Nitroimidazole drugs, such as metronidazole and tinidazole, are the only recommended treatment, but cases of resistance represent at least 5%. In case of resistance or therapeutic failure, posology with higher doses is used, culminating in the increase of the toxic effects of the treatment. In this context, the development of new drugs becomes an eminent necessity. Hologram quantitative structure-activity relationship (HQSAR) models using nitroimidazole derivatives were generated to discover the relationship between the different chemical structures and the activity against cells and the selectivity against susceptible and resistant strains. One model of each strain was chosen for interpretation, both showed good internal coefficient (q 2 LOO values: 0.607 for susceptible strain and 0.646 for resistant strain subsets) and great values in other internal and external validations metrics. From the contribution of fragments to HQSAR models, several differences between the most and least potent compounds were found: 5-nitroimidazole contributes positively while 4-nitroimidazole negatively. QSAR models employing random forest (RF-QSAR) machine learning technique were also built and a robust model was obtained from resistant strain activity prediction (q 2 LOO equals to 0.618). The constructed HQSAR and RF-QSAR models were employed to predict the activity of three newly planned nitroimidazole derivatives in the design of new drugs candidates against T. vaginalis strains., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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 IC 50 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., (© 2018 John Wiley & Sons A/S.)

Introduction: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

The antioxidant potential of a series of thiazolylhydrazone derivatives was investigated using three different methods namely DPPH, ABTS and FRAP assays. In general, the tested compounds showed higher or comparable activity to that of curcumin, used as positive control. Chemometric analyses demonstrated that the presence of hydrazone moiety is required for the activity of this class of compounds. From these results, compound 4 was identified as the most promising molecule and was then selected for further studies. The antiproliferative effect of compound 4 was evaluated, being active in three (T47D, MDA-MB-231 and SKMEL) of the six cancer cell lines tested, with IC 50 values ranging from 15.9 to 31.3 μM. Compound 4 exhibited no detectable cytotoxic effect on peripheral blood mononuclear cells (PBMC) when tested at a concentration of 100 μM, demonstrating good selectivity. From these results, it is possible to infer that there is a correlation between antioxidant capacity and anticancer effects., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC 50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q 2 LOO , Q 2 (F2) , and Q 2 (F3) ), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

Background: Obesity and insulin resistance/diabetes are important risk factors for cardiovascular diseases and demand safe and efficacious therapeutics., Objective: To assess the effects of a new thiazolidine compound-GQ-11-on obesity and insulin resistance induced by a diabetogenic diet in LDL receptor-deficient (LDLr -/- ) mice., Methods: Molecular docking simulations of GQ-11, PPARα and PPARγ structures were performed. Male C57BL/6J LDLr -/- mice fed a diabetogenic diet for 24 weeks were treated with vehicle, GQ-11 or pioglitazone or (20 mg/kg/day) for 28 days by oral gavage. Glucose tolerance test, insulin, HOMA-IR, adipokines (leptin, adiponectin) and the lipid profile were assessed after treatment. Adipose tissue was analysed by X-ray analysis and morphometry; gene and protein expression were evaluated by real-time PCR and western blot, respectively., Results: GQ-11 showed partial agonism to PPARγ and PPARα. In vivo, treatment with GQ-11 ameliorated insulin sensitivity and did not modify subcutaneous adipose tissue and body weight gain. In addition, GQ-11 restored adipokine imbalance induced by a diabetogenic diet and enhanced Glut-4 expression in the adipose tissue. Improved insulin sensitivity was also associated with lower levels of MCP-1 and higher levels of IL-10. Furthermore, GQ-11 reduced triglycerides and VLDL cholesterol and increased HDL-cholesterol by upregulation of Apoa1 and Abca1 gene expression in the liver., Conclusion: GQ-11 is a partial/dual PPARα/γ agonist that demonstrates anti-diabetic effects. Additionally, it improves the lipid profile and ameliorates chronic inflammation associated with obesity in atherosclerosis-prone mice.

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., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Background: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs., Objective: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ., Conclusion: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

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 (q 2  = 0.715, r 2  = 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., (© 2017 John Wiley & Sons A/S.)

Cancer cells can expand to other parts of body through blood system and nodes from a mechanism known as metastasis. Due to the large annual growth of cancer cases, various biological targets have been studied and related to this disorder. A very interesting target related to cancer is human epidermal growth factor receptor 2 (HER2). In this study, we analyzed the main intermolecular interactions between a drug used in the cancer treatment (5-fluorouracil) and HER2. Molecular modeling methods were also employed to assess the molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM and HOMO-LUMO energies of 5-FU. From the docking simulations it was possible to analyze the interactions that occur between some residues in the binding site of HER2 and 5-FU. To validate the choice of basis set that was used in the NBO and QTAIM analyses, theoretical calculations were performed to obtain FT-IR and UV/Vis spectra, and the theoretical results are consistent with the experimental data, showing that the basis set chosen is suitable. For the maximum λ from the theoretical calculation (254.89nm) of UV/Vis, the electronic transition from HOMO to LUMO occurs at 4.89eV. From NBO analyses, we observed interactions between Asp863 and 5-FU, i.e. the orbitals with high transfer of electrons are LP O 15 (donor NBO) and BD* (π) N 1 -H 10 (acceptor NBO), being that the value of this interaction is 7.72kcal/mol. Results from QTAIM indicate one main intermolecular H bond, which is necessary to stabilize the complex formed between the ligands and the biological target. Therefore, this study allowed a careful evaluation on the main structural, spectroscopic and electronic properties involved in the interaction between 5-FU and HER2, an important biological complex related to the cancer treatment., (Copyright © 2017 Elsevier B.V. All rights reserved.)