MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

Quantitative structure-insecticidal activity of essential oils on the human head louse (Pediculus humanus capitis).

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

Exploring the antiviral activity of α-ketoamides compounds through electronic structure calculations: a structure-activity relationship study.

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.

QSAR Studies, Synthesis, and Biological Evaluation of New Pyrimido-Isoquinolin-Quinone Derivatives against Methicillin-Resistant Staphylococcus aureus.

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.

Antibacterial activity of withanolides and their structure-activity relationship.

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.

Design, Synthesis, and Structure-Activity Relationship Studies of New Quinone Derivatives as Antibacterial Agents.

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.

Classifying micropollutants by environmental risk in groundwater using screening analysis associated to a hybrid multicriteria method combining (Q)SAR tools, fuzzy AHP and ELECTRE.

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.

Employing "Red Flags" to Fight the Most Neglected Diseases: Nitroaromatic as Still Suitable Tools to Treat Human and Veterinary Parasitosis.

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.

QSAR Modelling of Biological Activity in Cannabinoids with Quantum Similarity Combinations of Charge Fitting Schemes and 3D-QSAR.

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.

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.

Psilostachyins as trypanocidal compounds: Bioguided fractionation of Ambrosia tenuifolia chemically modified extract.

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.

Progress on open chemoinformatic tools for expanding and exploring the chemical space.

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.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications.

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

QSAR models for insecticidal properties of plant essential oils on the housefly (Musca domestica L.).

The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult M. domestica housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.

Design, synthesis and evaluation of cholinesterase hybrid inhibitors using a natural steroidal alkaloid as precursor.

To date, Alzheimer's disease is the most alarming neurodegenerative disorder worldwide. This illness is multifactorial in nature and cholinesterase inhibitors have been the ones used in clinical treatments. In this context, many of these drugs selectively inhibit the acetylcholinesterase enzyme interacting in both the active site and the peripheric anionic site. Besides, some agents have exhibited extensive benefits being able to co-inhibit butyrylcholinesterase. In this contribution, a strategy previously explored by numerous authors is reported; the synthesis of hybrid cholinesterase inhibitors. This strategy uses a molecule of recognized high inhibitory activity (tacrine) together with a steroidal alkaloid of natural origin using different connectors. The biological assays demonstrated the improvement in the inhibitory activity compared to the alkaloidal precursor, together with the reinforcement of the interactions in multiple sites of the enzymatic cavity. This strategy should be explored and exploited in this area. Docking and molecular dynamic studies were performed to explain enzyme-ligand interactions, assisting a structure-activity relationship analysis.

Synthesis, Antiprotozoal Activity, and Cheminformatic Analysis of 2-Phenyl-2H-Indazole Derivatives.

Indazole is an important scaffold in medicinal chemistry. At present, the progress on synthetic methodologies has allowed the preparation of several new indazole derivatives with interesting pharmacological properties. Particularly, the antiprotozoal activity of indazole derivatives have been recently reported. Herein, a series of 22 indazole derivatives was synthesized and studied as antiprotozoals. The 2-phenyl-2H-indazole scaffold was accessed by a one-pot procedure, which includes a combination of ultrasound synthesis under neat conditions as well as Cadogan's cyclization. Moreover, some compounds were derivatized to have an appropriate set to provide structure-activity relationships (SAR) information. Whereas the antiprotozoal activity of six of these compounds against E. histolytica, G. intestinalis, and T. vaginalis had been previously reported, the activity of the additional 16 compounds was evaluated against these same protozoa. The biological assays revealed structural features that favor the antiprotozoal activity against the three protozoans tested, e.g., electron withdrawing groups at the 2-phenyl ring. It is important to mention that the indazole derivatives possess strong antiprotozoal activity and are also characterized by a continuous SAR.

Synthetic peptide derived from Scorpion venom displays minimal toxicity and anti-infective activity in an animal model

Multidrug-resistant bacteria represent a global health problem increasingly leading to infections that are untreatable with our existing antibiotic arsenal. Therefore, it is critical to identify novel effective antimicrobials. Venoms represent an underexplored source of potential antibiotic molecules. Here, we engineered a peptide (IsCT1-NH2) derived from the venom of the scorpion Opisthacanthus madagascariensis, whose application as an antimicrobial had been traditionally hindered by its high toxicity. Through peptide design and the knowledge obtained in preliminary studies with single and double-substituted analogs, we engineered IsCT1 derivatives with multiple amino acid substitutions to assess the impact of net charge on antimicrobial activity and toxicity. We demonstrate that increased net charge (from +3 to +6) significantly reduced toxicity toward human erythrocytes. Our lead synthetic peptide, [A]1[K]3[F]5[K]8-IsCT1-NH2 (net charge of +4), exhibited increased antimicrobial activity against Gram-negative and Gram-positive bacteria in vitro and enhanced anti-infective activity in a mouse model. Mechanism of action studies revealed that the increased antimicrobial activity of our lead molecule was due, at least in part, to its enhanced ability to permeabilize the outer membrane and depolarize the cytoplasmic membrane. In summary, we describe a simple method based on net charge tuning to turn highly toxic venom-derived peptides into viable therapeutics.

Crystal structure of Leishmania mexicana cysteine protease B in complex with a high-affinity azadipeptide nitrile inhibitor.

Leishmania mexicana is an obligate intracellular protozoan parasite that causes the cutaneous form of leishmaniasis affecting South America and Mexico. The cysteine protease LmCPB is essential for the virulence of the parasite and therefore, it is an appealing target for antiparasitic therapy. A library of nitrile-based cysteine protease inhibitors was screened against LmCPB to develop a treatment of cutaneous leishmaniasis. Several compounds are sufficiently high-affinity LmCPB inhibitors to serve both as starting points for drug discovery projects and as probes for target validation. A 1.4 Å X ray crystal structure, the first to be reported for LmCPB, was determined for the complex of this enzyme covalently bound to an azadipeptide nitrile ligand. Mapping the structure-activity relationships for LmCPB inhibition revealed superadditive effects for two pairs of structural transformations. Therefore, this work advances our understanding of azadipeptidyl and dipeptidyl nitrile structure-activity relationships for LmCPB structure-based inhibitor design. We also tested the same series of inhibitors on related cysteine proteases cathepsin L and Trypanosoma cruzi cruzain. The modulation of these mammalian and protozoan proteases represents a new framework for targeting papain-like cysteine proteases.

Optimization strategy of single-digit nanomolar cross-class inhibitors of mammalian and protozoa cysteine proteases.

Cysteine proteases (CPs) are involved in a myriad of actions that include not only protein degradation, but also play an essential biological role in infectious and systemic diseases such as cancer. CPs also act as biomarkers and can be reached by active-based probes for diagnostic and mechanistic purposes that are critical in health and disease. In this paper, we present the modulation of a CP panel of parasites and mammals (Trypanosoma cruzi cruzain, LmCPB, CatK, CatL and CatS), whose inhibition by nitrile peptidomimetics allowed the identification of specificity and selectivity for a given CP. The activity cliffs identified at the CP inhibition level are useful for retrieving trends through multiple structure-activity relationships. For two of the cruzain inhibitors (10g and 4e), both enthalpy and entropy are favourable to Gibbs binding energy, thus overcoming enthalpy-entropy compensation (EEC). Group contribution of individual molecular modification through changes in enthalpy and entropy results in a separate partition on the relative differences of Gibbs binding energy (ΔΔG). Overall, this study highlights the role of CPs in polypharmacology and multi-target screening, which represents an imperative trend in the actual drug discovery effort.

An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme.

Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer's disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and [Formula: see text] values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.