Updating and profiling the natural product-likeness of Latin American compound libraries.

Compound databases of natural products play a crucial role in drug discovery and development projects and have implications in other areas, such as food chemical research, ecology and metabolomics. Recently, we put together the first version of the Latin American Natural Product database (LANaPDB) as a collective effort of researchers from six countries to ensemble a public and representative library of natural products in a geographical region with a large biodiversity. The present work aims to conduct a comparative and extensive profiling of the natural product-likeness of an updated version of LANaPDB and the individual ten compound databases that form part of LANaPDB. The natural product-likeness profile of the Latin American compound databases is contrasted with the profile of other major natural product databases in the public domain and a set of small-molecule drugs approved for clinical use. As part of the extensive characterization, we employed several chemoinformatics metrics of natural product likeness. The results of this study will capture the attention of the global community engaged in natural product databases, not only in Latin America but across the world.

A critical assessment of bioactive compounds databases.

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.

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

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

Approaches for enhancing the analysis of chemical space for drug discovery.

INTRODUCTION: Chemical space is a general conceptual framework that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space are emerging and evolving. AREAS COVERED: The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods. EXPERT OPINION: Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). Chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

Identification of known drugs as potential SARS-CoV-2 Mpro inhibitors using ligand- and structure-based virtual screening.

Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment - in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy - application of ligand- and structure-based virtual screening - using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.

Epigenetic Target Fishing with Accurate Machine Learning Models.

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2.

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

PSC-db: A Structured and Searchable 3D-Database for Plant Secondary Compounds.

Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included. PSC-db is freely available for public use and is hosted at the Universidad de Talca.

Do Drug-likeness Rules Apply to Oral Prodrugs?

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.

Virtual Screening of Natural Products Database.

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.

Fragment Library of Natural Products and Compound Databases for Drug Discovery.

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

Identification of New Targets and the Virtual Screening of Lignans against Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by the progressive disturbance in cognition and affects approximately 36 million people, worldwide. However, the drugs used to treat this disease are only moderately effective and do not alter the course of the neurodegenerative process. This is because the pathogenesis of AD is mainly associated with oxidative stress, and current drugs only target two enzymes involved in neurotransmission. Therefore, the present study sought to identify potential multitarget compounds for enzymes that are directly or indirectly involved in the oxidative pathway, with minimal side effects, for AD treatment. A set of 159 lignans were submitted to studies of QSAR and molecular docking. A combined analysis was performed, based on ligand and structure, followed by the prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The results showed that the combined analysis was able to select 139 potentially active and multitarget lignans targeting two or more enzymes, among them are c-Jun N-terminal kinase 3 (JNK-3), protein tyrosine phosphatase 1B (PTP1B), nicotinamide adenine dinucleotide phosphate oxidase 1 (NOX1), NADPH quinone oxidoreductase 1 (NQO1), phosphodiesterase 5 (PDE5), nuclear factor erythroid 2-related factor 2 (Nrf2), cycloxygenase 2 (COX-2), and inducible nitric oxide synthase (iNOS). The authors conclude that compounds (06) austrobailignan 6, (11) anolignan c, (19) 7-epi-virolin, (64) 6-[(2R,3R,4R,5R)-3,4-dimethyl-5-(3,4,5-trimethoxyphenyl)oxolan-2-yl]-4-methoxy-1,3-benzodioxole, (116) ococymosin, and (135) mappiodoinin b have probabilities that confer neuroprotection and antioxidant activity and represent potential alternative AD treatment drugs or prototypes for the development of new drugs with anti-AD properties.

Glutantßase: a database for improving the rational design of glucose-tolerant ß-glucosidases.

Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the ß-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production. Thus, ß-glucosidases have been targeted by several studies aiming to understand the mechanism of glucose tolerance, pH and thermal resistance for constructing more efficient enzymes. In this paper, we present a database of ß-glucosidase structures, called Glutantßase. Our database includes 3842 GH1 ß-glucosidase sequences collected from UniProt. We modeled the sequences by comparison and predicted important features in the 3D-structure of each enzyme. Glutantßase provides information about catalytic and conserved amino acids, residues of the coevolution network, protein secondary structure, and residues located in the channel that guides to the active site. We also analyzed the impact of beneficial mutations reported in the literature, predicted in analogous positions, for similar enzymes. We suggested these mutations based on six previously described mutants that showed high catalytic activity, glucose tolerance, or thermostability (A404V, E96K, H184F, H228T, L441F, and V174C). Then, we used molecular docking to verify the impact of the suggested mutations in the affinity of protein and ligands (substrate and product). Our results suggest that only mutations based on the H228T mutant can reduce the affinity for glucose (product) and increase affinity for cellobiose (substrate), which indicates an increment in the resistance to product inhibition and agrees with computational and experimental results previously reported in the literature. More resistant ß-glucosidases are essential to saccharification in industrial applications. However, thermostable and glucose-tolerant ß-glucosidases are rare, and their glucose tolerance mechanisms appear to be related to multiple and complex factors. We gather here, a set of information, and made predictions aiming to provide a tool for supporting the rational design of more efficient ß-glucosidases. We hope that Glutantßase can help improve second-generation biofuel production. Glutantßase is available at http://bioinfo.dcc.ufmg.br/glutantbase .

Computational screening for potential drug candidates against the SARS-CoV-2 main protease.

Background: SARS-CoV-2 is the causal agent of the current coronavirus disease 2019 (COVID-19) pandemic. They are enveloped, positive-sense, single-stranded RNA viruses of the Coronaviridae family. Proteases of SARS-CoV-2 are necessary for viral replication, structural assembly, and pathogenicity. The approximately 33.8 kDa M pro protease of SARS-CoV-2 is a non-human homologue and is highly conserved among several coronaviruses, indicating that M pro could be a potential drug target for Coronaviruses. Methods: Herein, we performed computational ligand screening of four pharmacophores (OEW, remdesivir, hydroxychloroquine and N3) that are presumed to have positive effects against SARS-CoV-2 M pro protease (6LU7), and also screened 50,000 natural compounds from the ZINC Database dataset against this protease target. Results: We found 40 pharmacophore-like structures of natural compounds from diverse chemical classes that exhibited better affinity of docking as compared to the known ligands. The 11 best selected ligands, namely ZINC1845382, ZINC1875405, ZINC2092396, ZINC2104424, ZINC44018332, ZINC2101723, ZINC2094526, ZINC2094304, ZINC2104482, ZINC3984030, and ZINC1531664, are mainly classified as beta-carboline, alkaloids, and polyflavonoids, and all displayed interactions with dyad CYS145 and HIS41 from the protease pocket in a similar way as other known ligands. Conclusions: Our results suggest that these 11 molecules could be effective against SARS-CoV-2 protease and may be subsequently tested in vitro and in vivo to develop novel drugs against this virus.

Toxicity and Alkaloid Profiling of the Skin of the Golfo Dulcean Poison Frog Phyllobates vittatus (Dendrobatidae).

Frogs in the genus Phyllobates are known for the presence of batrachotoxin, a highly toxic alkaloid, in their skin. Nevertheless, Phyllobates frogs from Costa Rica and Panama (P. lugubris and P. vittatus) are considered non-toxic, as they have been reported to harbor low concentrations of this alkaloid. However, the potential toxicity of Central American Phyllobates has not been assessed experimentally. Our goal was to determine the toxicity of the whole skin of P. vittatus, an endemic species from the Southeastern Pacific region of Costa Rica. We performed median lethal dose (LD50) tests in mice to determine general toxicity, and an irritant assay based on the behavioral responses of mice to subcutaneous injection, to determine differences in irritability, as a measure of toxicity, among three study localities. Using UPLC-ESI-QTOF, we obtained chemical profiles of the methanolic extract of frog skins. Due to the absence of mortality at the studied doses, we were unable to estimate LD50. However, we recorded a list of toxicity symptoms in mice that are consistent with cardiotoxic effects, and found that mice presented more symptoms at higher concentrations of skin extracts during the first hour of the LD50 assays, recovering completely at all doses by the end of the assay. On the other hand, we did not detect differences in irritability among studied localities. Additionally, we putatively identified three toxic alkaloids (Batrachotoxinin A, DHQ 251A and Lehm 275A). This study provides the first experimental data on the toxicity and associated symptoms in mice, as well as the chemical profile of the skin of P. vittatus. We suggest that the skin alkaloids of P. vitattus may confer a chemical defense towards predators.

In silico evaluation of pesticides as potential modulators of human DNA methyltransferases.

DNA methylations are carried out by DNA methyltransferases (DNMTs) that are key enzymes during gene expression. Many chemicals, including pesticides, have shown modulation of epigenetic functions by inhibiting DNMTs. In this work, human DNMTs were evaluated as a potential target for pesticides through virtual screening of 1038 pesticides on DNMT1 (3SWR) and DNMT3A (2QRV). Molecular docking calculations for DNMTs-pesticide complexes were performed using AutoDock Vina. Binding-affinity values and contact patterns were employed as selection criteria of pesticides as virtual hits for DNMTs. The best three DNMT-pesticides complexes selected according to their high absolute affinity values (kcal/mol), for both DNMT1 and DNMT3A, were flocoumafen (-12.5; -9.9), brodifacoum (-12.4; -8.4) and difenacoum (-12.1; -8.7). These chemicals belong to second-generation rodenticides. The most frequent predicted interacting residues for DNMT1-pesticide complexes were Trp1170A, Phe1145A, Asn1578A, Arg1574A and Pro1225A; whereas for DNMT3A those were Arg271B, Lys740A, and Glu303B. These results suggest that rodenticides used for pest control are potential DNMT ligands and therefore, may modulate DNA methylations. This finding has important environmental and clinical implications, as epigenetic pathways are critical in many biochemical processes leading to diseases.

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity.

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

Potential Inhibitors of the Activity of the Cholesterol-Ester Transfer Protein.

The cholesterol-ester transfer protein (CETP) exchanges lipids between high-density lipoproteins (HDLs) and low-density lipoproteins (LDLs). The excessive transport of lipids from HDLs to LDLs mediated by this protein can cause an alteration in the deposition of lipoproteins onto the arterial walls, thus promoting the development of arteriosclerosis. Different CETP inhibitors have been tested in recent years, but none has been confirmed as being effectively palliative for the disease. We employed in silico databases and molecular docking as a computational method to predict how potential CETP inhibitors could interact with the active site of the CETP protein. Upon previously comparing two computer software packages to determine which generated a greater number of accurate CETP-inhibitor-complex structures, we chose the more appropriate program for our studies. We then abstracted a series of databases of known CETP inhibitors and noninhibitors exhibiting different 50% concentrations of CETP-inhibitory (INH) activity, to generate virtual structures for docking with different combinations of the CETP receptor. From this process, we obtained as the most suitable structure 4F2A_1OB_C_PCW-it accordingly having a greater area under the receiver operating characteristic curve. The molecular docking of known compounds in comparison with the respective conformation of this inhibitor enabled us to obtain ΔGs (in kcal/mol) from which data we made a first exploration of unknown compounds for CETP-INH activity. Thus, the 4F2A_1OB_C_PCW structure was docked with DrugBank-Approved commercial compounds in an extensive database, whose status had already been established from pharmacokinetics and toxicology. In this study, we present a group of potential compounds as CETP-inhibitor candidates.