QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Overcoming class imbalance in drug discovery problems: Graph neural networks and balancing approaches

This research investigates the application of Graph Neural Networks (GNNs) to enhance the cost-effectiveness of drug development, addressing the limitations of cost and time. Class imbalances within classification datasets, such as the discrepancy between active and inactive compounds, give rise to difficulties that can be resolved through strategies like oversampling, undersampling, and manipulation of the loss function. A comparison is conducted between three distinct datasets using three different GNN architectures. This benchmarking research can steer future investigations and enhance the efficacy of GNNs in drug discovery and design. Three hundred models for each combination of architecture and dataset were trained using hyperparameter tuning techniques and evaluated using a range of metrics. Notably, the oversampling technique outperforms eight experiments, showcasing its potential. While balancing techniques boost imbalanced dataset models, their efficacy depends on dataset specifics and problem type. Although oversampling aids molecular graph datasets, more research is needed to optimize its usage and explore other class imbalance solutions.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Thioredoxin reductase selenoproteins from different organisms as potential drug targets for treatment of human diseases

Human thioredoxin reductase (TrxR) is a selenoprotein with a central role in cellular redox homeostasis, utilizing a highly reactive and solvent-exposed selenocysteine (Sec) residue in its active site. Pharmacological modulation of TrxR can be obtained with several classes of small compounds showing different mechanisms of action, but most often dependent upon interactions with its Sec residue. The clinical implications of TrxR modulation as mediated by small compounds have been studied in diverse diseases, from rheumatoid arthritis and ischemia to cancer and parasitic infections. The possible involvement of TrxR in these diseases was in some cases serendipitously discovered, by finding that existing clinically used drugs are also TrxR inhibitors. Inhibiting isoforms of human TrxR is, however, not the only strategy for human disease treatment, as some pathogenic parasites also depend upon Sec-containing TrxR variants, including S. mansoni, B. malayi or O. volvulus. Inhibiting parasite TrxR has been shown to selectively kill parasites and can thus become a promising treatment strategy, especially in the context of quickly emerging resistance towards other drugs. Here we have summarized the basis for the targeting of selenoprotein TrxR variants with small molecules for therapeutic purposes in different human disease contexts. We discuss how Sec engagement appears to be an indispensable part of treatment efficacy and how some therapeutically promising compounds have been evaluated in preclinical or clinical studies. Several research questions remain before a wider application of selenoprotein TrxR inhibition as a first-line treatment strategy might be developed. These include further mechanistic studies of downstream effects that may mediate treatment efficacy, identification of isoform-specific enzyme inhibition patterns for some given therapeutic compounds, and the further elucidation of cell-specific effects in disease contexts such as in the tumor microenvironment or in host-parasite interactions, and which of these effects may be dependent upon the specific targeting of Sec in distinct TrxR isoforms.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

First In Silico Screening of Insect Molecules for Identification of Novel Anti-Parasitic Compounds

Schistosomiasis is a neglected tropical disease caused by blood flukes of the genus Schistosoma. In silico screenings of compounds for the identification of novel anti-parasitic drug candidates have received considerable attention in recent years, including the screening of natural compounds. For the first time, we investigated molecules from insects, a rather neglected source in drug discovery, in an in silico screening approach to find novel antischistosomal compounds. Based on the Dictionary of Natural Products (DNP), we created a library of 1327 insect compounds suitable for molecular docking. A structure-based virtual screening against the crystal structure of a known druggable target in Schistosoma mansoni, the thioredoxin glutathione reductase (SmTGR), was performed. The top ten compounds predominantly originated from beetles and were predicted to interact particularly with amino acids in the doorstop pocket of SmTGR. For one compound from a jewel beetle, buprestin H, we tested and confirmed antischistosomal activity against adult and juvenile parasites in vitro. At concentrations with anti-parasitic activity, we could also exclude any unspecific cytotoxic activity against human HepG2 cells. This study highlights the potential of insect molecules for the identification of novel antischistosomal compounds. Our library of insect-derived molecules could serve not only as basis for future in silico screenings against additional target proteins of schistosomes, but also of other parasites.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits

With about 400,000 annual deaths worldwide, malaria remains a public health burden in tropical and subtropical areas, especially in low-income countries. Selection of drug-resistant Plasmodium strains has driven the need to explore novel antimalarial compounds with diverse modes of action. In this context, biodiversity has been widely exploited as a resourceful channel of biologically active compounds, as exemplified by antimalarial drugs such as quinine and artemisinin, derived from natural products. Thus, combining a natural product library and quantitative structure-activity relationship (QSAR)-based virtual screening, we have prioritized genuine and derivative natural compounds with potential antimalarial activity prior to in vitro testing. Experimental validation against cultured chloroquine-sensitive and multi-drug-resistant P. falciparum strains confirmed the potent and selective activity of two sesquiterpene lactones (LDT-597 and LDT-598) identified in silico. Quantitative structure-property relationship (QSPR) models predicted absorption, distribution, metabolism, and excretion (ADME) and physiologically based pharmacokinetic (PBPK) parameters for the most promising compound, showing that it presents good physiologically based pharmacokinetic properties both in rats and humans. Altogether, the in vitro parasite growth inhibition results obtained from in silico screened compounds encourage the use of virtual screening campaigns for identification of promising natural compound-based antimalarial molecules.

A multi-dimensional, time-lapse, high content screening platform applied to schistosomiasis drug discovery

Approximately 10% of the world's population is at risk of schistosomiasis, a disease of poverty caused by the Schistosoma parasite. To facilitate drug discovery for this complex flatworm, we developed an automated high-content screen to quantify the multidimensional responses of Schistosoma mansoni post-infective larvae (somules) to chemical insult. We describe an integrated platform to process worms at scale, collect time-lapsed, bright-field images, segment highly variable and touching worms, and then store, visualize, and query dynamic phenotypes. To demonstrate the methodology, we treated somules with seven drugs that generated diverse responses and evaluated 45 static and kinetic response descriptors relative to concentration and time. For compound screening, we used the Mahalanobis distance to compare multidimensional phenotypic effects induced by 1323 approved drugs. Overall, we characterize both known anti-schistosomals and identify new bioactives. Apart from facilitating drug discovery, the multidimensional quantification provided by this platform will allow mapping of chemistry to phenotype.

Deep Learning-driven research for drug discovery: Tackling Malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.

Network-based piecewise linear regression for QSAR modelling

Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening

Chagas disease is a neglected tropical disease (NTD) caused by the protozoan parasite Trypanosoma cruzi and is primarily transmitted to humans by the feces of infected Triatominae insects during their blood meal. The disease affects 6-8 million people, mostly in Latin America countries, and kills more people in the region each year than any other parasite-born disease, including malaria. Moreover, patient numbers are currently increasing in non-endemic, developed countries, such as Australia, Japan, Canada, and the United States. The treatment is limited to one drug, benznidazole, which is only effective in the acute phase of the disease and is very toxic. Thus, there is an urgent need to develop new, safer, and effective drugs against the chronic phase of Chagas disease. Using a QSAR-based virtual screening followed by in vitro experimental evaluation, we report herein the identification of novel potent and selective hits against T. cruzi intracellular stage. We developed and validated binary QSAR models for prediction of anti-trypanosomal activity and cytotoxicity against mammalian cells using the best practices for QSAR modeling. These models were then used for virtual screening of a commercial database, leading to the identification of 39 virtual hits. Further in vitro assays showed that seven compounds were potent against intracellular T. cruzi at submicromolar concentrations (EC₅₀ < 1 μM) and were very selective (SI > 30). Furthermore, other six compounds were also inside the hit criteria for Chagas disease, which presented activity at low micromolar concentrations (EC₅₀ < 10 μM) against intracellular T. cruzi and were also selective (SI > 15). Moreover, we performed a multi-parameter analysis for the comparison of tested compounds regarding their balance between potency, selectivity, and predicted ADMET properties. In the next studies, the most promising compounds will be submitted to additional in vitro and in vivo assays in acute model of Chagas disease, and can be further optimized for the development of new promising drug candidates against this important yet neglected disease.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents

The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite’s biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease.

The rise of resistance through the intensive use of drugs targeted to treat specific infectious diseases means that new therapeutics are continually required. Diseases common in the tropics and sub-tropics, classified as neglected tropical diseases, suffer from a lack of new drug treatments due to the difficulty in developing new drugs and the lack of market incentive. One such disease is schistosomiasis, a major human helminth disease caused by worms from the genus Schistosoma. It is currently treated by a 40-year old drug, praziquantel, but its widespread use has led to signs of drug-resistance emerging, with no alternative effective treatments available. To meet this challenge, we have developed an innovative computational drug repurposing pipeline supported by experimental phenotypic screening. Protein kinases emerged from our pipeline as interesting new biological targets. Given that many human kinase inhibitors have been successfully applied specially in cancer therapy and kinases have conserved structures and functions, we also undertook a detailed analysis of the kinases present in all infective Schistosoma species and human host. This allowed identification of new Schistosoma-specific kinase targets and suggest approved drugs to be used for treating schistosomiasis as well as opening new avenues to treat this neglected disease.

Computational quantum chemistry, molecular docking, and ADMET predictions of imidazole alkaloids of Pilocarpus microphyllus with schistosomicidal properties

Schistosomiasis affects million people and its control is widely dependent on a single drug, praziquantel. Computational chemistry has led to the development of new tools that predict molecular properties related to pharmacological potential. We conducted a theoretical study of the imizadole alkaloids of Pilocarpus microphyllus (Rutaceae) with schistosomicidal properties. The molecules of epiisopiloturine, epiisopilosine, isopilosine, pilosine, and macaubine were evaluated using theory models (B3lyp/SDD, B3lyp/6-31+G(d,p), B3lyp/6-311++G(d,p)). Absorption, distribution, metabolization, excretion, and toxicity (ADMET) predictions were used to determine the pharmacokinetic and pharmacodynamic properties of the alkaloids. After optimization, the molecules were submitted to molecular docking calculations with the purine nucleoside phosphorylase, thioredoxin glutathione reductase, methylthioadenosine phosphorylase, arginase, uridine phosphorylase, Cathepsin B1 and histone deacetylase 8 enzymes, which are possible targets of Schistosoma mansoni. The results showed that B3lyp/6-311++G(d,p) was the optimal model to describe the properties studied. Thermodynamic analysis showed that epiisopiloturine and epiisopilosine were the most stable isomers; however, the epiisopilosine ligand achieved a superior interaction with the enzymes studied in the molecular docking experiments, which corroborated the results of previous experimental studies on schistosomiasis.

Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models

Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).

Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling

A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.

QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

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

On the virtues of automated quantitative structure-activity relationship: the new kid on the block

Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.

Structural insights into natural compounds as inhibitors of Fasciola gigantica thioredoxin glutathione reductase

Fascioliasis is caused by the helminth parasites of genus Fasciola. Thioredoxin glutathione reductase (TGR) is an important enzyme in parasitic helminths and plays an indispensable role in its redox biology. In the present study, we conducted a structure-based virtual screening of natural compounds against the Fasciola gigantica TGR (FgTGR). The compounds were docked against FgTGR in four sequential docking modes. The screened ligands were further assessed for Lipinski and ADMET prediction so as to evaluate drug proficiency and likeness property. After refinement, three potential inhibitors were identified that were subjected to 50 ns molecular dynamics simulation and free energy binding analyses to evaluate the dynamics of protein-ligand interaction and the stability of the complexes. Key residues involved in the interaction of the selected ligands were also determined. The results suggested that three top hits had a negative binding energy greater than GSSG (-91.479 KJ · mol^-1 ), having -152.657, -141.219, and -92.931 kJ · mol^-1 for compounds with IDs ZINC85878789, ZINC85879991, and ZINC36369921, respectively. Further analysis showed that the compound ZINC85878789 and ZINC85879991 displayed substantial pharmacological and structural properties to be a drug candidate. Thus, the present study might prove useful for the future design of new derivatives with higher potency and specificity.

Chalcone Derivatives: Promising Starting Points for Drug Design

Medicinal chemists continue to be fascinated by chalcone derivatives because of their simple chemistry, ease of hydrogen atom manipulation, straightforward synthesis, and a variety of promising biological activities. However, chalcones have still not garnered deserved attention, especially considering their high potential as chemical sources for designing and developing new effective drugs. In this review, we summarize current methodological developments towards the design and synthesis of new chalcone derivatives and state-of-the-art medicinal chemistry strategies (bioisosterism, molecular hybridization, and pro-drug design). We also highlight the applicability of computer-assisted drug design approaches to chalcones and address how this may contribute to optimizing research outputs and lead to more successful and cost-effective drug discovery endeavors. Lastly, we present successful examples of the use of chalcones and suggest possible solutions to existing limitations.

Synthesis and biological evaluation of novel imidazolidine derivatives as candidates to schistosomicidal agents

INTRODUCTION:

Schistosomiasis is an infectious parasitic disease caused by trematodes of the genus Schistosoma, which threatens at least 258 million people worldwide and its control is dependent on a single drug, praziquantel. The aim of this study was to evaluate the anti-Schistosoma mansoni activity in vitro of novel imidazolidine derivatives.

MATERIAL AND METHODS:

We synthesized two novel imidazolidine derivatives: (LPSF/PTS10) (Z)-1-(2-chloro-6-fluorobenzyl)-4-(4-dimethylaminobenzylidene)-5-thioxoimidazolidin-2-one and (LPSF/PTS23) (Z)-1-(2-chloro-6-fluoro-benzyl)-5-thioxo-4-(2,4,6-trimethoxy-benzylidene)-imidazolidin-2-one. The structures of two compounds were determined by spectroscopic methods. During the biological assays, parameters such as motility, oviposition, mortality and analysis by Scanning Electron Microscopy were performed.

RESULTS:

LPSF/PTS10 and LPSF/PTS23 were considered to be active in the separation of coupled pairs, mortality and to decrease the motor activity. In addition, LPSF/PTS23 induced ultrastructural alterations in worms, after 24 h of contact, causing extensive erosion over the entire body of the worms.

CONCLUSION:

The imidazolidine derivatives containing the trimetoxy and benzylidene halogens showed promising in vitro schistosomicidal activity.

Computer-Aided Drug Design Using Sesquiterpene Lactones as Sources of New Structures with Potential Activity against Infectious Neglected Diseases

This review presents an survey to the biological importance of sesquiterpene lactones (SLs) in the fight against four infectious neglected tropical diseases (NTDs)—leishmaniasis, schistosomiasis, Chagas disease, and sleeping sickness—as alternatives to the current chemotherapies that display several problems such as low effectiveness, resistance, and high toxicity. Several studies have demonstrated the great potential of some SLs as therapeutic agents for these NTDs and the relationship between the protozoal activities with their chemical structure. Recently, Computer-Aided Drug Design (CADD) studies have helped increase the knowledge of SLs regarding their mechanisms, the discovery of new lead molecules, the identification of pharmacophore groups and increase the biological activity by employing in silico tools such as molecular docking, virtual screening and Quantitative-Structure Activity Relationship (QSAR) studies.

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.