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Background: Neurological disorders are composed of several diseases that affect the central and peripheral nervous system; among these are neurodegenerative diseases, which lead to neuronal death. Many of these diseases have treatment for the disease and symptoms, leading pa-tients to use several drugs that cause side effects. Introduction: The search for new treatments has led to the investigation of multi-target drugs. Method: This review aimed to investigate in the literature the multi-target effect in neurological disorders through an in silico approach. Studies were reviewed on the diseases such as epilepsy, Alzheimer's disease, Amyotrophic Lateral Sclerosis (ALS), Huntington's disease, cerebral ischemia, and Parkinson's disease. Result: As a result, the study emphasize the relevance of research by computational techniques such as quantitative structure-activity relationship (QSAR) prediction models, pharmacokinetic pre-diction models, molecular docking, and molecular dynamics, besides presenting possible drug can-didates with multi-target activity. Conclusion: It was possible to identify several targets with pharmacological activities. Some of these targets had diseases in common such as carbonic anhydrase, acetylcholinesterase, NMDA, and MAO being relevant for possible multi-target approaches.

Introduction: Although drugs currently available for the treatment of anxiety and de-pression act through modulation of the neurotransmission systems involved in the neurobiology of the disorder, yet they often present side effects, which can impair patient adherence to treatment. Methods: This has driven the search for new molecules with anxiolytic and antidepressant potential. Aromatic plants are rich in essential oils, and their chemical constituents, such as monoterpenes, are being studied for these disorders. This study aims to evaluate the anxiolytic and antidepressant-like potential of the monoterpene tetrahydrolinalool in in vivo animal models and review pharmacologi-cal targets with validation through molecular docking. Male Swiss mice (Mus musculus) were treat-ed with THL (37.5-600 mg kg-1 p.o.) and submitted to the elevated plus maze, open field, rotarod, and forced swim tests. In the elevated plus-maze, THL at doses of 37.5 and 75 mg kg-1 induced a significant increase in the percentage of entries (72.7 and 64.3% respectively), and lengths of stay (80.3 and 76.8% respectively) in the open arms tests. Results: These doses did not compromise locomotor activity or motor coordination in the animals. In the open field, rotarod tests, and the forced swimming model, treatment with THL significantly reduced immobility times at doses of 150, 300, and 600 mg kg-1, and by respective percentages of 69.3, 60.9 and 68.7%. Conclusion: In molecular docking assay, which investigated potential targets, THL presented satis-factory energy values for: nNOs, SGC, IL-6, 5-HT1A, NMDAr, and D1. These demonstrate the po-tential of THL (a derivative of natural origin) in in vivo and in silico models, making it a drug can-didate.

Natural products are compounds isolated from plants that provide a variety of lead structures for the development of new drugs by the pharmaceutical industry. The interest in these substances increases because of their beneficial effects on human health. Alzheimer's disease (AD ) affects occur in about 80% of individuals aged 65 years. AD is the most common cause of dementia in elderly people, is characterized by progressive neurodegenerative alterations, as decrease of cholinergic impulse, increased toxic effects caused by reactive oxygen species and the inflammatory process that the amyloid plaque participates. In silico studies is relevant in the process of drug discovery; through technological advances in the areas of structural characterization of molecules, computational science and molecular biology have contributed to the planning of new drugs used against neurodegenerative diseases. Considering the social impairment caused by increased incidence of disease and that there is no chemotherapy treatment effective against the AD; several compounds are studied. In the researches for effective neuroprotectants as potential treatments for Alzheimer's disease, natural products that have been extensively studied in various AD models. This study aims to carry out a literature review with articles that address the In silico studies of natural products aimed at potential drugs against alzheimer's disease (AD) in the period from 2015 to 2021.

Background: COVID-19 is still causing long-term health consequences, mass deaths, and collapsing healthcare systems around the world. There are no efficient drugs for its treatment. However, previous studies revealed that SARS-CoV-2 and SARS-CoV have 96% and 86.5% similarities in cysteine proteases (3CLpro) and papain-like protease (PLpro) sequences, respectively. This resemblance could be important in the search for drug candidates with antiviral effects against SARS-CoV-2. Objective: This paper is a compilation of natural products that inhibit SARS-CoV 3CLpro and PLpro and, concomitantly, reduce inflammation and/or modulate the immune system as a perspective strategy for COVID-19 drug discovery. It also presents in silico studies performed on these selected natural products using SARS-CoV-2 3CLpro and PLpro as targets to propose a list of hit compounds. Method: The plant metabolites were selected in the literature based on their biological activities on SARS-CoV proteins, inflammatory mediators, and immune response. The consensus docking analysis was performed using four different packages. Results: Seventy-nine compounds reported in the literature with inhibitory effects on SARS-CoV proteins were reported as anti-inflammatory agents. Fourteen of them showed immunomodulatory effects in previous studies. Five and six of these compounds showed significant in silico consensus as drug candidates that can inhibit PLpro and 3CLpro, respectively. Our findings corroborated recent results reported on anti-SARS-CoV-2 in the literature. Conclusion: This study revealed that amentoflavone, rubranoside B, savinin, psoralidin, hirsutenone, and papyriflavonol A are good drug candidates for the search of antibiotics against COVID-19.

Sesquiterpene lactone (SL) subtypes including hirsutinolide and cadinanolide have a controversial genesis. Metabolites of these classes are either described as natural products or as artifacts produced via the influence of solvents, chromatographic mobile phases, and adsorbents used in phytochemical studies. Based on this divergence, and to better understand the sensibility of these metabolites, different pH conditions were used to prepare semisynthetic SLs and evaluate the anti-inflammatory and antiproliferative activities. Therefore, glaucolide B (1) was treated with various Brønsted–Lowry and Lewis acids and bases—the same approach was applied to some of its derivatives—allowing us to obtain 14 semisynthetic SL derivatives, 10 of which are hereby reported for the first time. Hirsutinolide derivatives 7a (CC50 = 5.0 µM; SI = 2.5) and 7b (CC50 = 11.2 µM; SI = 2.5) and the germacranolide derivative 8a (CC50 = 3.1 µM; SI = 3.0) revealed significant cytotoxic activity and selectivity against human melanoma SK-MEL-28 cells when compared with that against non-tumoral HUVEC cells. Additionally, compounds 7a and 7c.1 showed strongly reduced interleukin-6 (IL-6) and nitrite (NOx) release in pre-treated M1 macrophages J774A.1 when stimulated with lipopolysaccharide. Despite the fact that hirsutinolide and cadinanolide SLs may be produced via plant metabolism, this study shows that acidic and alkaline extraction and solid-phase purification processes can promote their formation.

Background: Alzheimer's disease (AD) is a very common neurodegenerative disorder in individuals over 65 years of age; however, younger individuals can also be affected due to brain damage. Introduction: The general symptoms of this disease include progressive loss of memory, changes in behavior, deterioration of thinking, and gradual loss of ability to perform daily activities. According to the World Health Organization, dementia has affected more than 50 million people worldwide, and it is estimated that there are 10 million new cases per year, of which 70% are due to AD. Methods: This paper reported a review of scientific articles available on the internet which discuss in silico analyzes such as molecular docking, molecular dynamics, and quantitative structure-activity relationship (QSAR) of different classes of natural products and their derivatives published from 2016 onwards. In addition, this work reports the potential of fermented papaya preparation against oxidative stress in AD. Results: This research reviews the most recent studies on AD, computational analysis methods used in proposing new bioactive compounds and their possible molecular targets, and finally, the molecules or classes of natural products involved in each study. Conclusion: Thus, studies like this can orientate new research works on neurodegenerative diseases, especially AD.

Constant research on natural products has generated, over time, a large number of compounds with the potential to be evaluated in several biological tests and subsequently have been cataloged in databases that allow other researchers to perform virtual screenings of activity in various biological systems. This considerably reduces the time for the development of new drugs. This review describes the main databases of natural products available for searching bioactive compounds. It also describes the main features of virtual screening strategies for the identification of molecules with the potential to be used as new drugs. In addition, a search was made in the Web of Science database, using the search term "Virtual screening of natural products databases" from 2003 to 2018. The search criterion resulted in 230 articles, which had their abstracts evaluated with pertinence to the criteria required for this work, which are: a) be a research article; b) performing a virtual screening on databases of natural products or containing natural products; and c) works that identified drug candidate molecules. Based on these criteria, the bibliographic review on the topic was excluded. After this analysis, 104 works were selected for this review. We selected relevant papers describing the potential drug candidates that were distributed in 15 classes, of which the anticancer, antibacterial and anti-inflammatory hits were the most abundant. The works showing efforts to search for new molecules against various other diseases in distinct biological systems were also described. In this way, this work shows an overview of several methodologies and we hope they can help and inspire the development of new research to improve people's quality of life.

Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.

Background: Natural products, such as phenylpropanoids, which are found in essential oils derived from aromatic plants, have been explored during non-clinical psychopharmacology studies, to discover new molecules with relevant pharmacological activities in the central nervous system, especially antidepressant and anxiolytic activities. Major depressive disorder is a highly debilitating psychiatric disorder and is considered to be a disabling public health problem, worldwide, as a primary factor associated with suicide. Current clinically administered antidepressants have late-onset therapeutic actions, are associated with several side effects, and clinical studies have reported that some patients do not respond well to treatment or reach complete remission. Objective: To review important new targets for antidepressant activity and to select phenylpropanoids with antidepressant activity, using Molegro Virtual Docker and Ossis Data Warris, and to verify substances with more promising antidepressant activity. Results and Conclusion: An in silico molecular modeling study, based on homology, was conducted to determine the three-dimensional structure of the 5-hydroxytryptamine 2A receptor (5- HT2AR), then molecular docking studies were performed and the predisposition for cytotoxicity risk among identified molecules was examined. A model for 5-HT2AR homology, with satisfactory results, was obtained indicating the good stereochemical quality of the model. The phenylpropanoid 4-allyl-2,6-dimethoxyphenol showed the lowest binding energy for 5-HT2AR, with results relevant to the L-arginine/nitric oxide (NO)/cGMP pathway, and showed no toxicity within the parameters of mutagenicity, carcinogenicity, reproductive system toxicity, and skin-tissue irritability, when evaluated in silico; therefore, this molecule can be considered promising for the investigation of antidepressant activity.

Background: Epilepsy is a neurological disease affected by an imbalance of inhibitory and excitatory signaling in the brain. Introduction: In this disease, the targets are active in pathophysiology and thus can be used as a focus for pharmacological treatment. Methods: Several studies demonstrated the antiepileptic effect of drugs acting on the following tar-gets: N-methyl-D-aspartate (NMDA), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, voltage-gated calcium channel (Cav), Gamma aminobutyric acid transporter type 1 (GAT1), voltage-gated sodium channels (Nav), voltage-gated potassium channel of the Q subfam-ily (KCNQ) and Gamma aminobutyric acid type A (GABAA) receiver. Results: These studies highlight the importance of molecular docking. Conclusion: Quantitative Structure-Activity Relationship (QSAR) and computer aided drug design (CADD) in predicting of possible pharmacological activities of these targets.

Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.

Background: Tuberculosis is a disease with high incidence and high mortality rate, especially in Brazil. Although there are several medications available for treatment, in cases of resistance, there is a need to use more than one medication. Objective: Therefore, cases of toxicity increase and reports of resistance have been worrying the population. In addition, some medications have a short period of effectiveness. To achieve the goal, ligand-based and structure-based approaches were used. Methods: Thus, in an attempt to discover potent inhibitors against Mycobacterium tuberculosis enzymes, we sought to identify natural products with high therapeutic potential for the treatment of Tuberculosis through QSAR, Molecular Modeling and ADMET studies. Results: The results showed that the models generated from two sets of molecules with known activity against M. tuberculosis enzymes InhA and PS were able to select 11 and 8 compounds, respectively, between Lignans and Neolignans with 50 to 60% activity probability. In addition, molecular docking contributed to confirm the mechanism of action of compounds and increase the accuracy of methodologies. All molecules showed higher binding energy values for the drug Isoniazid. We conclude that compounds 33, 34, 110, 114 and 133 are promising for InhA target and compounds 07, 08, 19, 21, 42, 48, 75 and 141 for target PS. In addition, most molecules did not show any toxicity according to the evaluated parameters. Conclusions: Therefore, Lignans and Neolignans may be an alternative for the treatment of Tuberculosis.

Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with highthroughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal. : In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology. : A systematical search using various databases was performed. The search was limited to articles published in the last 10 years. : The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better “drugability”.

Background: Chemoinformatics has several applications in the field of drug design, helping to identify new compounds against a range of ailments. Among these are Leishmaniasis, effective treatments for which are currently limited. Objective: To construct new indole 2-aminothiophene molecules using computational tools and to test their effectiveness against Leishmania amazonensis (sp.). Methods: Based on the chemical structure of thiophene-indol hybrids, we built regression models and performed molecular docking, and used these data as bases for design of 92 new molecules with predicted pIC50 and molecular docking. Among these, six compounds were selected for the synthesis and to perform biological assays (leishmanicidal activity and cytotoxicity). Results: The prediction models and docking allowed inference of characteristics that could have positive influences on the leishmanicidal activity of the planned compounds. Six compounds were synthesized, one-third of which showed promising antileishmanial activities, with IC50 ranging from 2.16 and 2.97 μM (against promastigote forms) and 0.9 and 1.71 μM (against amastigote forms), with selectivity indexes (SI) of 52 and 75. Conclusion: These results demonstrate the ability of Quantitative Structure-Activity Relationship (QSAR)-based rational drug design to predict molecules with promising leishmanicidal potential, and confirming the potential of thiophene-indole hybrids as potential new leishmanial agents.

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Background: Nanotechnology has contributed a great deal to the field of medical science. Smart drugdelivery vectors, combined with stimuli-based characteristics, are becoming increasingly important. The use of external and internal stimulating factors can have enormous benefits and increase the targeting efficiency of nanotechnology platforms. The pH values of tumor vascular tissues are acidic in nature, allowing the improved targeting of anticancer drug payloads using drug-delivery vectors. Nanopolymers are smart drug-delivery vectors that have recently been developed and recommended for use by scientists because of their potential targeting capabilities, non-toxicity and biocompatibility, and make them ideal nanocarriers for personalized drug delivery. Method: The present review article provides an overview of current advances in the use of nanoparticles (NPs) as anticancer drug-delivery vectors. Results: This article reviews the molecular basis for the use of NPs in medicine, including personalized medicine, personalized therapy, emerging vistas in anticancer therapy, nanopolymer targeting, passive and active targeting transports, pH-responsive drug carriers, biological barriers, computer-aided drug design, future challenges and perspectives, biodegradability and safety. Conclusions: This article will benefit academia, researchers, clinicians, and government authorities by providing a basis for further research advancements.

Background: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas. Aim: This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools. Methods: A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG). Results: Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus. Conclusion: It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Background: Tuberculosis (Mycobacterium tuberculosis) is an infectious bacterial disease with the highest levels of mortality worldwide, presenting numerous cases of resistance. In silico studies, which elaborate chemical and biological models in computational tools and make it possible to interpret molecular characteristics, are among the methods used in the search for new drugs. Objective: In this perspective, our aim was to use QSAR and molecular modeling to propose possible pharmacophores from benzothiazinone derivatives. Methods: In this study, a set of 69 benzothiazinone derivatives, together with computational tools such as molecular descriptor analysis in chemometrics, metabolic prediction, and molecular coupling to 4 proteins: DprE1, InhA, PS, and DHFR important for the bacillus were investigated. Results: The chemometric model computed in the Volsurf+ program presented good predictive values for both amphiphilicity and molecular volume. These are essential for biological activity. Metabolites from the cytochrome isoforms CYP3A4 and 2D6 interactions revealed coupling divergences which, noting that the metabolites did not present changes to the QSAR proposed pharmacophore structures, may be due to the reaction medium and existing differences in the benzothiazinone structures. Similarly, molecular docking with the four TB enzymes presented good interactions for the more active compounds. The fragments found using QSAR (being essential for biological activity) also presented as being essential for ligand-protein site interactions. Conclusion: From the benzothiazinone derivative series evaluated, compound 11026134 presented the best profile in all study analyses, noting that the trifluoromethyl, nitro group, and piperazine fragment with aliphatic hydrocarbon groups are likely pharmacophores for the benzothiazinones studied.

Backgound:Exploring potent compounds is critical to generating multi-target drug discovery. Hematin crystallization is an important mechanism of malaria.Methods:A series of chloroquine analogues were designed using a repositioning approach to develop new anticancer compounds. Protein-ligand interaction fingerprints and ADMET descriptors were used to assess docking performance in virtual screenings to design chloroquine hybrid β-hematin inhibitors. A PLS algorithm was applied to correlate the molecular descriptors to IC50 values. The modeling presented excellent predictive power with correlation coefficients for calibration and cross-validation of r2 = 0.93 and q2 = 0.72. Using the model, a series of 4-aminoquinlin hybrids were synthesized and evaluated for their biological activity as an external test series. These compounds were evaluated for cytotoxic cell lines and β-hematin inhibition.Results:The target compounds exhibited high β-hematin inhibition activity and were 3-9 times more active than the positive control. Furthermore, all the compounds exhibited moderate to high cytotoxic activity. The most potent compound in the dataset was docked with hemoglobin and its pharmacophore features were generated. These features were used as input to the Pharmit server for screening of six databases.Conclusion:The compound with the best score from ChEMBL was 2016904, previously reported as a VEGFR-2 inhibitor. The 11 compounds selected presented the best Gold scores with drug-like properties and can be used for drug development.

Eriocaulaceae is a pantropical family whose main center of biodiversity is in Brazil. In general, the family has about 1200 species, in which phytochemical and biological studies have shown a variety of structures and activities. The aim of this research is to compile the compounds isolated in the Eriocaulaceae family and carry out a computational study on their biological targets. The bibliographic research was carried out on six databases. Tables were built and organized according to the chemical class. In addition, a summary of the methods of isolating the compounds was also made. In the computational study were used ChEMBL platform, DRAGON 7.0, and the KNIME 4.4.0 software. Two hundred and twenty-two different compounds have been isolated in sixty-eight species, divided mainly into flavonoids and naphthopyranones, and minor compounds. The ligand-based virtual screening found promising molecules and molecules with multitarget potential, such as xanthones 194, 196, 200 and saponin 202, with xanthone 194 as the most promising. Several compounds with biological activities were isolated in the family, but the chemical profiles of many species are still unknown. The selected structures are a starting point for further studies to develop new antiparasitic and antiviral compounds based on natural products.

Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in-house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars-CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.

Natural products and their secondary metabolites are promising starting points for the development of drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. State-of-the-art, curated, integrated, and frequently updated databases of secondary metabolites are thus highly relevant to drug discovery. The SistematX Web Portal, introduced in 2018, is undergoing development to address this need and documents crucial information about plant secondary metabolites, including the exact location of the species from which the compounds were isolated. SistematX also allows registered users to log in to the data management area and gain access to administrative pages. This study reports recent updates and modifications to the SistematX Web Portal, including a batch download option, the generation and visualization of 1H and 13C nuclear magnetic resonance spectra, and the calculation of physicochemical (drug-like and lead-like) properties and biological activity profiles. The SistematX Web Portal is freely available at http://sistematx.ufpb.br.

Background:Tubulin polymerization inhibitors interfere with microtubule assembly and their functions lead to mitotic arrest, therefore they are attractive target for design and development of novel anticancer compounds.Objective:The proposed novel and effective structures following the use of three-dimensionalquantitative structure activity relationship (3D-QSAR) pharmacophore based virtual screening clearly demonstrate the high efficiency of this method in modern drug discovery.Method:Combined computational approach was applied to extract the essential 2D and 3D features requirements for higher activity as well as identify new anti-tubulin agents.Results:The best quantitative pharmacophore model, Hypo1, exhibited good correlation of 0.943 (RMSD=1.019) and excellent predictive power in the training set compounds. Generated model AHHHR, was well mapped to colchicine site and three-dimensional spatial arrangement of their features were in good agreement with the vital interactions in the active site. Total prediction accuracy (0.92 for training set and 0.86 for test set), enrichment factor (4.2 for training set and 4.5 for test set) and the area under the ROC curve (0.86 for training set and 0.94 for the test set), the developed model using Extended Class FingerPrints of maximum diameter 4 (ECFP_4) was chosen as the best model.Conclusion:Developed computational platform provided a better understanding of requirement features for colchicine site inhibitors and we believe the results of this study might be useful for the rational design and optimization of new inhibitors.

Background: Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat. Objective: The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders. Methods: In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles. Discussion: In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies. Conclusion: In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

Diet is an essential factor affecting the development of and risk for diabetes mellitus. In search of preventative and therapeutic strategies, the potential role of certain foods and their bioactive compounds to prevent the pathogenesis associated with metabolic diseases is to be considered. Human consumption of anthocyanins is among the highest of all flavonoids. Epidemiological studies have suggested that the consumption of anthocyanins lowers the risk of diabetes and diabetic complications. Anthocyanins are important natural bioactive pigments responsible for red to blue colour of fruits, leaves, seeds, stems and flowers, which are present in a variety of plant species particularly in berries and cherries. A large number of bioactive anthocyanins, such as cyanidin, malvidin, delphinidin, pelargonidin, peonidin, petunidin and their metabolites have shown multiple biological activities with apparent effects on glucose absorption, glucose uptake, insulin secretion and sensitivity, on the enzymes involved in glucose metabolism, gene expressions, inflammatory mediators, glucose transporters in progression of diabetes and associated complications, such as diabetic retinopathy, nephropathy, neuropathy and diabetic vascular diseases. The versatility of the anthocyanins provides a promising approach for diabetes management than synthetic drugs. Here we summarize the effect of several anthocyanins on many in vitro, in vivo and clinical studies and also reveal the mechanisms which could prevent or reverse the underlying mechanisms of diabetic pathologies including promotion of antioxidant, antihyperlipidemic, anti-inflammatory and anti-apoptotic activities.

Dengue like any neglected tropical disease affects a large part of the world population. In this disease, the infection is caused by arboviruses transmitted by the A. aegypti and A. albopictus mosquito, in which its most severe manifestation is known as dengue hemorrhagic fever. The infected person presents symptoms characteristic of such as fever and rash. Among the ways of fighting dengue by bioactives is the inhibition of NS2B-NS3 protease, inhibition of protein E, and inhibition of sclerotization of the vector cuticle. The cuticle is indispensable for the survival of the mosquito that can be compromised through the inhibition of arylalkylamine N-acetyltransferase (aaNAT). In the studies shown, in silico tests were performed as molecular docking, functional density analysis, molecular orbitals energies with the analyses of the interactions between bioactives and the targets studied. However, in addition to discussing the fight against dengue virus infection through different routes, in this paper, some in silico results of 27 analogs of myricetin have been presented, which showed action on the cuticle sclerotization mechanism.

Background: Neglected diseases require special attention when looking for new therapeutic alternatives, as these are diseases of extreme complexity and severity that affect populations belonging to lower social classes who are lacking access to basic rights such as basic sanitation. Introduction: Among the alternatives available for obtaining new drugs is Medicinal Chemistry, which is responsible for the discovery, identification, invention and preparation of prototypes. In this perspective, the present work aims to make a bibliographic review on the recent studies of Medicinal Chemistry applied to neglected diseases. Method: A literature review was carried out by searching the “Web of Sciences” database, including recent articles published in the Neglected Drug Design. Results: In general, it was noticed that the most studied neglected diseases corresponded to Chagas disease and leishmaniasis, with studies on organic synthesis, optimization of structures and molecular hybrids being the most used strategies. It is also worth mentioning the growing number of computationally developed studies, providing speed and optimization of costs in the procurement process. Conclusion: The CADD approach and organic synthesis studies, when applied in the area of Medicinal Chemistry proved to be important in the research and discovery of drugs for Neglected Diseases, since the planning of the experimental methodology used to obtain it, as well as in the selection of compounds with greater activity potential.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC50) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

Parasitic diseases remain as unresolved health issues worldwide. While for some parasites the treatments involve drug combinations with serious side effects, for others, chemical therapies are inefficient due to the emergence of drug resistance. This urges the search for novel antiparasitic agents able to act through multiple mechanisms of action. Here, we report the first multi-target model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mt-QSAR-MLP) to virtually design and predict versatile inhibitors of proteins involved in the survival and/or infectivity of different pathogenic parasites. The mt-QSAR-MLP model exhibited high accuracy (>80%) in both training and test sets for the classification/prediction of protein inhibitors. Several fragments were directly extracted from the physicochemical and structural interpretations of the molecular descriptors in the mt-QSAR-MLP model. Such interpretations enabled the generation of four molecules that were predicted as multi-target inhibitors against at least three of the five parasitic proteins reported here with two of the molecules being predicted to inhibit all the proteins. Docking calculations converged with the mt-QSAR-MLP model regarding the multi-target profile of the designed molecules. The designed molecules exhibited drug-like properties, complying with Lipinski’s rule of five, as well as Ghose’s filter and Veber’s guidelines.

Background: Selective and reversible types of MAO-B inhibitors have emerged as promising candidates for the management of neurodegenerative diseases. Several functionalized chalcone derivatives were shown to have potential reversible MAO-B inhibitory activity, which have recently been reported from our laboratory. Methods: With the experimental results of about 70 chalcone derivatives, we further developed a pharmacophore modelling, and 2D and 3D- QSAR analyses of these reported chalcones for MAOB inhibition. Results: The 2D-QSAR model presented four variables (MATS7v, GATS 1i and 3i, and C-006) from 143 Dragon 7 molecular descriptors, with a r2 value of 0.76 and a Q2 cv for cross-validation equal to 0.72. An external validation also was performed using 11 chalcones, obtaining a Q2 ext value of 0.74. The second 3D-QSAR model using MLR (multiple linear regression) was built starting from 128 Volsurf+ molecular descriptors, being identified as 4 variables (Molecular descriptors): D3, CW1 and LgS11, and L2LGS. Adetermination coefficient (r2) value of 0.76 and a Q2 cv for cross-validation equal to 0.72 were obtained for this model. An external validation also was performed using 11 chalcones and a Q2 ext value of 0.74 was found. Conclusion: This report exhibited a good correlation and satisfactory agreement between experiment and theory.

Background: The growing demand for agricultural products has led to the misuse/overuse of insecticides, resulting in the use of higher concentrations and the need for ever more toxic products. Ecologically, bioinsecticides are considered better and safer than synthetic insecticides, they must be toxic to the target organism, yet with low or no toxicity to non-target organisms. Many plant extracts have seen their high insecticide potential confirmed under laboratory conditions, and in the search for plant compounds with bioinsecticidal activity, the Lamiaceae family has yielded satisfactory results. Objective: The aim of our study was to develop computer-assisted predictions for compounds with known insecticidal activity against Aphis gossypii and Drosophila melanogaster. Results and conclusion: Structure analysis revealed ent-kaurane, kaurene, and clerodane diterpenes as the most active, showing excellent results. We also found that the interactions formed by these compounds were more stable, or presented similar stability to the commercialized insecticides tested. Overall, we concluded that the compounds bistenuifolin L (1836) and bistenuifolin K (1931), were potentially active against A. gossypii enzymes, and salvisplendin C (1086) and salvixalapadiene (1195), are potentially active against D. melanogaster. We observed and highlight that the diterpenes bistenuifolin L (1836), bistenuifolin K (1931), salvisplendin C (1086), and salvixalapadiene (1195), present a high probability of activity and low toxicity against the species studied.

Objective: This study evaluated the antifungal activity of cinnamaldehyde on Candida spp. In vitro and in situ assays were carried out to test cinnamaldehyde for its anti-Candida effects, antibiofilm activity, effects on fungal micromorphology, antioxidant activity, and toxicity on keratinocytes and human erythrocytes. Statistical analysis was performed considering &alpha, = 5%. Results: The minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) of cinnamaldehyde ranged from 18.91 &mu, M to 37.83 &mu, M. MIC values did not change in the presence of 0.8 M sorbitol, whereas an 8-fold increase was observed in the presence of ergosterol, suggesting that cinnamaldehyde may act on the cell membrane, which was subsequently confirmed by docking analysis. The action of cinnamaldehyde likely includes binding to enzymes involved in the formation of the cytoplasmic membrane in yeast cells. Cinnamaldehyde-treated microcultures showed impaired cellular development, with an expression of rare pseudo-hyphae and absence of chlamydoconidia. Cinnamaldehyde reduced biofilm adherence by 64.52% to 33.75% (p &lt, 0.0001) at low concentrations (378.3&ndash, 151.3 &micro, M). Cinnamaldehyde did not show antioxidant properties. Conclusions: Cinnamaldehyde showed fungicidal activity through a mechanism of action likely related to ergosterol complexation, it was non-cytotoxic to keratinocytes and human erythrocytes and showed no antioxidant activity.

Leishmaniasis is a complex disease caused by over 20 Leishmania species that primarily affects populations with poor socioeconomic conditions. Currently available drugs for treating leishmaniasis include amphotericin B, paromomycin, and pentavalent antimonials, which have been associated with several limitations, such as low efficacy, the development of drug resistance, and high toxicity. Natural products are an interesting source of new drug candidates. The Asteraceae family includes more than 23 000 species worldwide. Secondary metabolites that can be found in species from this family have been widely explored as potential new treatments for leishmaniasis. Recently, computational tools have become more popular in medicinal chemistry to establish experimental designs, identify new drugs, and compare the molecular structures and activities of novel compounds. Herein, we review various studies that have used computational tools to examine various compounds identified in the Asteraceae family in the search for potential drug candidates against Leishmania.

Gastrointestinal stromal tumors (GISTs) are unusual cancers which begin in specialized cells in the gastrointestinal tract wall. Various strategies involving single-agents, combinations, and rapid complementary inhibitor cycling are now being used to control such tumors. Based on promising early clinical trial experience, certain novel KIT, and PDGFRA tyrosine kinase inhibitors have begun advanced clinical development. Resistance to tyrosine kinase inhibitors has brought immense difficulties, with patients now requiring additional therapeutic options. This review describes and discusses the last five years (2016-2020) in developing novel c-KIT kinase inhibitors using virtual screening and docking approaches. Computational techniques can be used to complement experimental studies to identify new candidate molecules for therapeutic use. Molecular modeling strategies allow analysis of the required characteristics that compounds must have to effectively bind c-KIT. Through such analyses, it is possible to both discover and design novel inhibitors against cancer-related proteins that play a critical role in tumor development (including of mutant strains). Docking showed potential in the detection of the key residues responsible for ligand recognition and are very helpful to understand the interactions in the active site that can be used to develop new compounds/classes of anticancer drugs and help millions of cancer patients.

Aim: Selenium-based compounds have antitumor potential. We used a ligand-based virtual screening analysis to identify selenoglycolicamides with potential antitumor activity. Results & Conclusion: Compounds 3, 6, 7 and 8 were selected for in vitro cytotoxicity tests against various cell lines, according to spectrophotometry results. Compound 3 presented the best cytotoxicity results against a promyelocytic leukemia line (HL-60) and was able to induce cell death at a frequency similar to that observed for doxorubicin. The docking study showed that compound 3 has good interaction energies with the targets caspase-3, 7 and 8, which are components of the apoptotic pathway. These results suggested that selenium has significant pharmacological potential for the selective targeting of tumor cells, inducing molecular and cellular events that culminate in tumor cell death.

Chronic orofacial pain is a serious public health problem with a prevalence of 7-11% in the population. This disorder has different etiologies and characteristics that make pharmacological treatment difficult. Natural products have been shown to be a promising source of treatments for the management of chronic pain, as an example the terpenes.The aim of this study was to evaluate the anti-nociceptive and anti-inflammatory effects of one of these terpenes, d-limonene (LIM - a common monoterpene found in citrus fruits) alone and complexed with hydroxypropyl-β-cyclodextrin (LIM/HPβCD) in preclinical animal models.Orofacial pain was induced by the administration of hypertonic saline on the corneal surface, the injection of formalin into the temporomandibular joint (TMJ), or chronic constriction injury of the infraorbital nerve (CCI-IoN). The study used male Wistar rats and Swiss mice treated with LIM (50 mg/kg), LIM/HPβCD (50 mg/kg), vehicle (control), gabapentin or morphine, and eyes wiping (induced by hypertonic saline), face rubbing (formalin-induced in TMJ) or mechanical hyperalgesia (provoked by CCI-IoN) were assessed. Additionally, ELISA was used to measure TNF-α, and western blot analysis to assess levels of PKAcα, NFκB, p38MAPK and phosphorylated PKC substrates. Serum levels of aspartate aminotransferase (AST) and alanine transferase (ALT) were also evaluated.LIM and LIM/HPβCD significantly reduced (p 0.001) corneal nociception and formalin-induced TMJ nociception. In addition, both substances attenuated (p 0.001) mechanical hyperalgesia in the CCI-IoN model. The antinociceptive effect induced by LIM and HPβCD/LIM was associated with decreased TNF-α levels, downregulation of the NFκB and p38MAPK signalling pathways and reduced PKC substrate phosphorylation and PKA immunocontent. Moreover, the results demonstrated that complexation with HPβCD was able to decrease the therapeutic dose of LIM.LIM was found to be a promising molecule for the treatment of orofacial pain due to its capacity to modulate some important mediators essential to the establishment of pain, and HPβCD can be a key tool to improve the profile of LIM.

Background: Cyclin-dependent kinase 4 (CDK4) and the human epidermal growth factor receptor 2 (HER2) are two of the most promising targets in oncology research. Thus, a series of computational approaches have been applied to the search for more potent inhibitors of these cancerrelated proteins. However, current approaches have focused on chemical analogs while predicting the inhibitory activity against only one of these targets, but never against both. Aims: We report the first perturbation model combined with machine learning (PTML) to enable the design and prediction of dual inhibitors of CDK4 and HER2. Methods: Inhibition data for CDK4 and HER2 were extracted from ChEMBL. The PTML model relied on artificial neural networks to allow the classification/prediction of molecules as active or inactive against CDK4 and/or HER2. Results: The PTML model displayed sensitivity and specificity higher than 80% in the training set. The same statistical metrics had values above 75% in the test set. We extracted several molecular fragments and estimated their quantitative contributions to the inhibitory activity against CDK4 and HER2. Guided by the physicochemical and structural interpretations of the molecular descriptors in the PTML model, we designed six molecules by assembling several fragments with positive contributions. Three of these molecules were predicted as potent dual inhibitors of CDK4 and HER2, while the other three were predicted as inhibitors of at least one of these proteins. All the molecules complied with Lipinski’s rule of five and its variants. Conclusion: The present work represents an encouraging alternative for future anticancer chemotherapies.