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In recent years, the scientific community has worked intensively in the search and development of new drugs to suppress viral infections, such as COVID-19. In fact, a number of active compounds have been tested; however, the absence of significant structure-activity relationships hinders the production of optimized drugs. In this study, molecular modeling techniques were employed to investigate the electronic, structural and chemical reactivity properties of a set α-ketoamides whose antiviral activities have been reported in the literature, aiming to propose new promising derivatives. The local reactivity of the compounds was evaluated via condensed-to-atoms Fukui indexes and molecular electrostatic potential. Multivariate data analysis and random forests machine learning techniques were employed to correlate the antiviral properties and electronic and structural descriptors and identify relevant variables. A series of new derivatives were then proposed and evaluated via density functional theory-based calculations, and docking/molecular dynamics with the target protein of the virus. The results suggest that active derivatives present reduced reactivity towards electrophilic agents on the central core of the molecules and high reactivity on R1 ligands. Derivatives with higher predicted antiviral activities were proposed based on simple electronic descriptors, and their efficacies are reinforced by docking and molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

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According to the WHO, antimicrobial resistance is among the top 10 threats to global health. Due to increased resistance rates, an increase in the mortality and morbidity of patients has been observed, with projections of more than 10 million deaths associated with infections caused by antibacterial resistant microorganisms. Our research group has developed a new family of pyrimido-isoquinolin-quinones showing antibacterial activities against multidrug-resistant Staphylococcus aureus. We have developed 3D-QSAR CoMFA and CoMSIA studies (r2 = 0.938; 0.895), from which 13 new derivatives were designed and synthesized. The compounds were tested in antibacterial assays against methicillin-resistant Staphylococcus aureus and other bacterial pathogens. There were 12 synthesized compounds active against Gram-positive pathogens in concentrations ranging from 2 to 32 µg/mL. The antibacterial activity of the derivatives is explained by the steric, electronic, and hydrogen-bond acceptor properties of the compounds.

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Two new withanolides, (17R,20S,22R)-4ß-acetoxy-5ß,6ß-epoxy-19,27-dihydroxy-1-oxo-witha-2,24-dienolide (withalongolide A 4-acetate (5) and (17R,20S,22R)-5ß,6ß-epoxy-27-hydroxy-1,4-dioxo-witha-24-enolide (9), and seven known withanolides with normal structure (1-4, 6-8) were isolated from aerial parts of Cuatresia colombiana. Several semisynthetic derivatives were prepared from the natural metabolites withaferin A and jaborosalactone 38. The compounds were fully characterized by a combination of spectroscopic methods (1D and 2D NMR and MS). The compounds isolated from C. colombiana, sixteen withanolides previously isolated from different Solanaceae species with different skeletons and semisynthetic derivatives were evaluated for their antibacterial activity against a selected panel of Gram-positive and Gram-negative bacteria. According to the bioactivity against S. aureus and E. faecalis, the compounds evaluated were divided into three groups: compounds with high activity (MIC 0.063 mM), compounds with moderate activity (0.5 mM > MIC > 0.125 mM) and non-active compounds (MIC ≥1 mM); in addition, some structure-activity relationship keys could be inferred.

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Resistance to antibacterial agents is a growing global public health problem that reduces the efficacy of available antibacterial agents, leading to increased patient mortality and morbidity. Unfortunately, only 16 antibacterial drugs have been approved by the FDA in the last 10 years, so it is necessary to develop new agents with novel chemical structures and/or mechanisms of action. In response to this, our group takes up the challenge of designing a new family of pyrimidoisoquinolinquinones displaying antimicrobial activities against multidrug-resistant Gram-positive bacteria. Accordingly, the objective of this study was to establish the necessary structural requirements to obtain compounds with high antibacterial activity, along with the parameters controlling antibacterial activity. To achieve this goal, we designed a family of compounds using different strategies for drug design. Forty structural candidates were synthesized and characterized, and antibacterial assays were carried out against high-priority bacterial pathogens. A variety of structural properties were modified, such as hydrophobicity and chain length of functional groups attached to specific carbon positions of the quinone core. All the synthesized compounds inhibited Gram-positive pathogens in concentrations ranging from 0.5 to 64 µg/mL. Two derivatives exhibited minimum inhibitory concentrations of 64 µg/mL against Klebsiella pneumoniae, while compound 28 demonstrated higher potency against MRSA than vancomycin.

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This study aimed to evaluate and monitor pesticides in groundwater of the Serra Geral aquifer, located in the Paraná Basin 3 (southern Brazil), using Liquid Chromatography coupled with a Quadrupole-Time-of-Flight Mass Spectrometer (LC-QTOF MS). A total of 117 samples, collected in three different moments, were analyzed over 36 months. Groundwater samples from 35 wells and four surface water points were monitored in each sampling campaign. A pesticide screening methodology was proposed with the tentative identification of 1607 pesticides and pesticide metabolites. The application of the proposed methodology resulted in the verification of 29 pesticides and pesticide metabolites, 7 as confirmed analytes and 22 as suspect compounds. (Q)SAR in silico predictions and GUS index calculation provided data on the potential environmental risk of the identified compounds, with eight endpoints considered. After in silico predictions, an alternative hybrid multicriteria method was applied, combining the weighting of endpoints of fuzzy AHP and micropollutants classification by environmental risk using ELECTRE. The fuzzy AHP results indicated the greatest importance of mutagenicity among the eight evaluated indicators, while the scarce influence of the physicochemical properties on the environmental risk suggested their exclusion from the model. Accordingly, the ELECTRE results highlighted the prevalence of thiamethoxam and carbendazim as the most dangerous for the environment. The application of the proposed method enabled the selection of the compounds that must be monitored, considering mutagenicity and toxicity predictions for environmental risk analysis.

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Quantitative structure-activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi.nlm.nih.gov/] database provided the geometries, binding affinities(Ki ) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD50 ) to breast cancer cells. An innovative quantum similarity approach combining (self)-similarity indexes calculated with different charge-fitting schemes under the Topo-Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R2 ) and leave-one-out cross-validation[Q2 (LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD50 : R2 =0.9666 and Q2 (LOO)=0.9312; pKi (CB1): R2 =1.0000 and Q2 (LOO)=0.9727, and pKi (CB2): R2 =0.9996 and Q2 (LOO)=0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similarity-based descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D-QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand-based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.

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Nitroaromatic compounds have been used for treating parasitic diseases since the 1960s. Pharmacological alternatives to treat them are under observation. However, for the most neglected diseases, such as those caused by worms and less known protozoans, nitro compounds are still among the drugs of choice, despite their well-known collateral effects. In this review, we describe the chemistry and the uses of the still most employed nitroaromatic compounds for treating parasitosis caused by worms or lesser-known protozoans. We also describe their application as veterinary drugs. The most accepted mechanism of action seems to be the same, leading to collateral effects. For this reason, a special session was dedicated to discussing toxicity, carcinogenicity, and mutagenesis, as well as the most acceptable aspects of the known structure-activity/toxicity relationships involving nitroaromatic compounds. It employed the SciFindern search tool from the American Chemical Society in the search for the most relevant bibliography within the field, exploring keyword expressions such as "NITRO COMPOUNDS" and "BIOLOGICAL ACTIVITY" (within Abstracts or Keywords) and concepts related to parasites, pharmacology and toxicology. The results were classified according to the chemical classes of nitro compounds, being the most relevant studies regarding journal impact and interest of the described results chosen to be discussed. From the found literature, it is easy to notice that nitro compounds, especially the nitroaromatic ones, are still widely used in antiparasitic therapy, despite their toxicity. They also are the best starting point in the search for new active compounds.

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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.

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This work focusses on the chemical diversification of an Ambrosia tenuifolia extract and its bioguided fractionation, aiming to unveil the chemical entity responsible for the trypanocidal activity. Besides, a revision of the phytochemical study of this species, based on previous reports of the antiparasitic psilostachyins A and C as main compounds, was conducted. To improve the biological properties of a plant extract through a simple chemical reaction, the oxidative diversification of the dichloromethane extract of this plant species was carried out. A bioguided fractionation of a chemically modified extract was performed by evaluating the inhibitory activity against Trypanosoma cruzi trypomastigotes. This experiment led to the isolation of one of the most active compounds. In general terms, epoxidized metabolites were obtained as a result of the oxidation of the major metabolite of the species. The trypanocidal activity of some tested metabolites overperformed the reference drug, benznidazole, displaying no cytotoxicity at trypanocidal concentrations. Key structure-activity relationships were obtained for designing previously undescribed antiparasitic sesquiterpene lactones.

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The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

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