Virtual Screening Procedure Research Articles

A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning

The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.

Identification of 1-phenyl-4-cyano-5-aminopyrazoles as novel ecdysone receptor ligands by virtual screening, structural optimization, and biological evaluations.

Ecdysteroids initiate the molting process in insects by binding to the ecdysone receptor (EcR), which is a promising target for identifying insect growth regulators. This paper presents an in silico/in vitro screening procedure for identifying new EcR ligands. The three-step virtual screening procedure uses a three-dimensional pharmacophore model, docking and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) rescoring routine. A novel hit (VS14) with good binding activity against Plutella xylostella EcR was identified from a library of over 200,000 chemicals. Subsequently, the 1-phenyl-4-cyano-5-aminopyrazole scaffold and twelve EcR ligands were synthesized. Their IC50 values against Plutella xylostella EcR ranged from 0.64 to 23.21μm. Furthermore, a preliminary analysis of the structure-activity relationship for novel scaffolds provided a basis for designing new ligands with improved activity.

Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors.

The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma. We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines. The whole six compounds displayed good effects when compared with erlotinib at 30 μM. When reducing the concentration to 10μM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 μM, one compound showed inhibiting effects close to those from erlotinib. Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.

Anti-HCV and anti-malaria agent, potential candidates to repurpose for coronavirus infection: Virtual screening, molecular docking, and molecular dynamics simulation study

AimsCoronavirus disease 2019 (COVID-19) has appeared in Wuhan, China but the fast transmission has led to its widespread prevalence in various countries, which has made it a global concern. Another concern is the lack of definitive treatment for this disease. The researchers tried different treatment options which are not specific. The current study aims to identify potential small molecule inhibitors against the main protease protein of SARS-CoV-2 by the computational approach. Main methodsIn this study, a virtual screening procedure employing docking of the two different datasets from the ZINC database, including 1615 FDA approved drugs and 4266 world approved drugs were used to identify new potential small molecule inhibitors for the newly released crystal structure of main protease protein of SARS-CoV-2. In the following to validate the docking result, molecular dynamics simulations were applied on selected ligands to identify the behavior and stability of them in the binding pocket of the main protease in 150 nanoseconds (ns). Furthermore, binding energy using the MMPBSA approach was also calculated. Key findingsThe result indicates that simeprevir (Hepatitis C virus NS3/4A protease inhibitor) and pyronaridine (antimalarial agent) could fit well to the binding pocket of the main protease and because of some other beneficial features including broad-spectrum antiviral properties and ADME profile, they might be a promising drug candidate for repurposing to the treatment of COVID-19. SignificanceSimeprevir and pyronaridine were selected by the combination of virtual screening and molecular dynamics simulation approaches as a potential candidate for treatment of COVID-19.

Computational drug repurposing for the identification of SARS-CoV-2 main protease inhibitors

Accepted 7 July 2020ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the known COVID-19 disease. Since currently no definitive therapies or vaccines for the SARS-CoV-2 virus are available, there is an urgent need to identify effective drugs against SARS-CoV-2 infection. One of the best-known targets available is the main protease of this virus, crucial for the processing of polyproteins codified by viral RNA. In this work, we used a computational virtual screening procedure for the repurposing of commercial drugs available in the DrugBank database as inhibitors of the SARS-CoV-2 main protease. Molecular docking calculations and molecular dynamics (MD) simulations have been applied. The computational model was validated through a self-docking procedure. The screening procedure highlighted five interesting drugs that showed a comparable or higher docking score compared to the crystallographic compound and maintained the protein binding during the MD runs. Amongst these drugs, Ritonavir has been used in clinical trials with patients affected by COVID-19 and Nelfinavir showed anti-SARS-CoV-2 activity. The five identified drugs could be evaluated experimentally as inhibitors of the SARS-CoV-2 main protease in view of a possible COVID-19 treatment. Communicated by Ramaswamy H. Sarma

Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

Virtual screening of approved drugs as potential SARS-CoV-2 main protease inhibitors

The global emergency caused by COVID-19 makes the discovery of drugs capable of inhibiting SARS-CoV-2 a priority, to reduce the mortality and morbidity of this disease. Repurposing approved drugs can provide therapeutic alternatives that promise rapid and ample coverage because they have a documented safety record, as well as infrastructure for large-scale production. The main protease of SARS-CoV-2 (Mpro) is an excellent therapeutic target because it is critical for viral replication; however, Mpro has a highly flexible active site that must be considered when performing computer-assisted drug discovery. In this work, potential inhibitors of the main protease (Mpro) of SARS-Cov-2 were identified through a docking-assisted virtual screening procedure. A total of 4384 drugs, all approved for human use, were screened against three conformers of Mpro. The ligands were further studied through molecular dynamics simulations and binding free energy analysis. A total of nine currently approved molecules are proposed as potential inhibitors of SARS-CoV-2. These molecules can be further tested to speed the development of therapeutics against COVID-19.

Virtual screening for potential inhibitors of β(1,3)-D-glucan synthase as drug candidates against fungal cell wall

Background To enhance the outcome in patients with invasive candidiasis, initiation of an efficient antifungal treatment in a suitable dosage is necessary. Echinocandins (e.g. caspofungin) inhibit the enzyme β(1,3)-D-glucan synthase of the fungal cell wall. Compared to azoles and other antifungal agents, echinocandins have lower adverse effects and toxicity in humans. Echinocandins are available in injectable (intravenous) form. Methods In this study, to identify the novel oral drug-like compounds that affect the fungal cell wall, downloaded oral drug-like compounds from the ZINC database were processed with a virtual screening procedure. The docking free energies were calculated and compared with the known inhibitor caspofungin. Four molecules were selected as the most potent ligands and subjected to hydrogen bonds analysis. Results Considering the hydrogen bond analysis, two compounds (ZINC71336662 and ZINC40910772) were predicted to better interact with the active site of β(1,3)-D-glucan synthase compared with caspofungin. Conclusion The introduced compound in this study may be valuable to analyze experimentally as a novel oral drug candidate targeting fungal cell walls.

Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach.

Singlet exciton fission is a mechanism that could potentially enable solar cells to surpass the Shockley-Queisser efficiency limit by converting single high-energy photons into two lower-energy triplet excitons with minimal thermalization loss. The ability to make use of singlet exciton fission to enhance solar cell efficiencies has been limited, however, by the sparsity of singlet fission materials with triplet energies above the bandgaps of common semiconductors such as Si and GaAs. Here, we employ a high-throughput virtual screening procedure to discover new organic singlet exciton fission candidate materials with high-energy (>1.4 eV) triplet excitons. After exploring a search space of 4482 molecules and screening them using time-dependent density functional theory, we identify 88 novel singlet exciton fission candidate materials based on anthracene derivatives. Subsequent purification and characterization of several of these candidates yield two new singlet exciton fission materials: 9,10-dicyanoanthracene (DCA) and 9,10-dichlorooctafluoroanthracene (DCOFA), with triplet energies of 1.54 eV and 1.51 eV, respectively. These materials are readily available and low-cost, making them interesting candidates for exothermic singlet exciton fission sensitization of solar cells. However, formation of triplet excitons in DCA and DCOFA is found to occur via hot singlet exciton fission with excitation energies above ∼3.64 eV, and prominent excimer formation in the solid state will need to be overcome in order to make DCA and DCOFA viable candidates for use in a practical device.

Predictive Power of In Silico Approach to Evaluate Chemicals against M. tuberculosis: A Systematic Review.

Mycobacterium tuberculosis (Mtb) is an endemic bacterium worldwide that causes tuberculosis (TB) and involves long-term treatment that is not always effective. In this context, several studies are trying to develop and evaluate new substances active against Mtb. In silico techniques are often used to predict the effects on some known target. We used a systematic approach to find and evaluate manuscripts that applied an in silico technique to find antimycobacterial molecules and tried to prove its predictive potential by testing them in vitro or in vivo. After searching three different databases and applying exclusion criteria, we were able to retrieve 46 documents. We found that they all follow a similar screening procedure, but few studies exploited equal targets, exploring the interaction of multiple ligands to 29 distinct enzymes. The following in vitro/vivo analysis showed that, although the virtual assays were able to decrease the number of molecules tested, saving time and money, virtual screening procedures still need to develop the correlation to more favorable in vitro outcomes. We find that the in silico approach has a good predictive power for in vitro results, but call for more studies to evaluate its clinical predictive possibilities.

Identification of novel inhibitors of p-hydroxyphenylpyruvate dioxygenase using receptor-based virtual screening

Abstract p-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is a potential target for herbicide discovery, which catalyzes the conversion of p-hydroxyphenylpyruvate to homogentisate. Triketone HPPD inhibitors have been widely applied to weed control for many years. To discover potential HPPD inhibitors with novel scaffold, a virtual screening procedure combining pharmacophore model, molecular docking and molecular dynamics (MD) simulation was carried out. The crystal complex (PDB: 6ISD ) with sulcotrione was selected to establish the receptor-ligand pharmacophore model (CBP). The verified CBP model with key features and interactions was employed to seek structures with potential inhibitory from ZINC database. The best-fit compounds from pharmacophore screening (2050 compounds) were subsequently subjected to molecular docking with CDOCKER method to identify the binding affinity of inhibitors with amino acid residues in active pocket. Six compounds were obtained as the final hits with the Fe(Ш) coordination and π-π interaction with Phe381 and Phe424. MD results demonstrated that Phe424 made great contributions to binding affinities of all the systems. This work provided powerful insights into the design and development of novel HPPD inhibitor in silico techniques. ZINC000255329531 was identified as the most promising candidate due to its high HPPD inhibition effect (ΔGbind = 25.66 kcal mol−1), in comparison with the commercial triketone HPPD herbicide sulcotrione (ΔGbind = 18.94 kcal mol−1).

New factor Xa inhibitors based on 1,2,3,4-tetrahydroquinoline developed by molecular modelling

Factor Xa is a serine protease representing a crucial element in the coagulation process and an attractive target for anticoagulant therapy. At the present time there are several chemical classes of factor Xa inhibitors with proven activity. Furthermore, three factor Xa inhibitors have been approved for the medical use to date. However, therapy with these medications is accompanied by substantial adverse effects. In this background, the structure-based computational approach combining molecular docking and semiempirical quantum chemical calculations was applied for a search for new effective factor Xa inhibitors. We have undertaken a few virtual screening procedures to select potential candidates for synthesis and subsequent testing. The first screen of the focused library resulted in identifying 20 compounds among which 7 compounds showed the noticeable inhibition of factor Xa at maximal concentrations, allowed by solubility. The subsequent additional screens identified 20 additional candidates. Of these, 5 substances were shown to be capable of inhibiting factor Xa at 5 μM. The best two found 1,2,3,4-tetrahydroquinoline derivatives identified by means of modelling have demonstrated IC50 values in the micromolar range. One of them turned out to be selective factor Xa inhibitor over trypsin, factors IIa, IXa and XIa.

Multi-task generative topographic mapping in virtual screening.

The previously reported procedure to generate "universal" Generative Topographic Maps (GTMs) of the drug-like chemical space is in practice a multi-task learning process, in which both operational GTM parameters (example: map grid size) and hyperparameters (key example: the molecular descriptor space to be used) are being chosen by an evolutionary process in order to fit/select "universal" GTM manifolds. After selection (a one-time task aimed at optimizing the compromise in terms of neighborhood behavior compliance, over a large pool of various biological targets), for any further use the manifolds are ready to provide "fit-free" predictive models. Using any structure-activity set-irrespectively whether the associated target served at map fitting stage or not-the generation or "coloring" a property landscape enables predicting the property for any external molecule, with zero additional fitable parameters involved. While previous works have signaled the excellent behavior of such models in aggressive three-fold cross-validation assessments of their predictive power, the present work wished to explore their behavior in Virtual Screening (VS), here simulated on hand of external DUD ligand and decoy series that are fully disjoint from the ChEMBL-extracted landscape coloring sets. Beyond the rather robust results of the universal GTM manifolds in this challenge, it could be shown that the descriptor spaces selected by the evolutionary multi-task learner were intrinsically able to serve as an excellent support for many other VS procedures, starting from parameter-free similarity searching, to local (target-specific) GTM models, to parameter-rich, nonlinear Random Forest and Neural Network approaches.

Gp41 inhibitory activity prediction of theaflavin derivatives using ligand/structure-based virtual screening approaches

Gp41 and its conserved hydrophobic groove on the NHR region is one of the attractive targets in the design of HIV-1 entry inhibitory agents. This hydrophobic pocket is very critical for the progression of HIV and host cell fusion. In this study different ligand-based (structure similarity search) and structure-based (molecular docking and molecular dynamic simulation) methods were performed in a virtual screening procedure to select the best compounds with the most probable HIV-1 gp41 inhibitory activities. In silico pharmacokinetics and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties filtration also was considered to choose the compounds with best drug-like properties. The results of molecular docking and molecular dynamic simulations of the final selected compounds showed suitable stabilities of their complexes with gp41. The final selected hits could have better pharmacokinetics properties than the template compound, theaflavin digallate (TF3), a naturally-originated potent gp41 inhibitor.

Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors

The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain “Big Data” for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure-activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.

Discovery of Novel Glucagon Receptor Antagonists UsingCombined Pharmacophore Modeling and Docking.

Glucagon and the glucagon receptor are most important molecules control over blood glucose concentrations. These two molecules are very important to studies of type 2 diabetic patients. In literature, several classes of small molecule antagonists of the human glucagon receptor have been reported. Glucagon receptor antagonist could decrease hepatic glucose output and improve glucose control in diabetic patients. In this research, to identify novel and diverse leads for use in potent glucagon receptor antagonist design, a ligand-based pharmacophore modeling, was developed using the best conformations of training set compounds. The best five features pharmacophore model, called Hypo1, includes, hydrogen bond acceptors, two hydrophobic, and positive ionizable features, which has the highest correlation coefficient (0.805), cost difference (64.38), low RMS (2.148), as well as it shows a high goodness of fit and enrichment factor. The generated pharmacophore model has been validated by using a series of similar structures with varying affinities for the glucagon receptor. Then, the developed model has been applied as a search query in different database searching with the main objective of finding novel molecules which have the potential to be be modified into novel lead compounds. As a result, some hit molecules were introduced as final candidates by employing virtual screening and molecular docking procedure simultaneously. The results from pharmacophore modeling and molecular docking are complementary to each other and could serve as a useful way for the discovery of potent small molecules as glucagon receptor antagonist.

Discovery and development of novel salicylate synthase (MbtI) furanic inhibitors as antitubercular agents

We report on the virtual screening, synthesis, and biological evaluation of new furan derivatives targeting Mycobacterium tuberculosis salicylate synthase (MbtI). A receptor-based virtual screening procedure was applied to screen the Enamine database, identifying two compounds, I and III, endowed with a good enzyme inhibitory activity. Considering the most active compound I as starting point for the development of novel MbtI inhibitors, we obtained new derivatives based on the furan scaffold. Among the SAR performed on this class, compound 1a emerged as the most potent MbtI inhibitor reported to date (Ki = 5.3 μM). Moreover, compound 1a showed a promising antimycobacterial activity (MIC99 = 156 μM), which is conceivably related to mycobactin biosynthesis inhibition.

Redox Flow Battery Electrolyte Design Formulations: Molecular Screening and Engineering

Redox flow battery (RFB) offers great promise to provide transformational progress for large-scale energy storage technology. As the energy-bearing redox active species are dissolved and externally stored in liquid electrolytes, their physiochemical properties dictate the energy density, specific capacity, and cycle life thereby become a single most critical component of RFB. For instance, the concentration and chemical stability of redox active species within the electrolyte will determine the system-specific energy density and temperature window for the RFB operation, respectively1. With the growing interest in developing sustainable RFB systems, there is a pressing task to identify the optimal electrolyte. This led to an active search for an ideal electrolyte which can hold a higher concentration of redox active species with wide chemical and thermal stability. Most electrolyte research has been focused on the empirical study of the effects of the pH, additives and solvent composition2. Using combined spectroscopic and computational studies we established an electrolyte design formulation based on molecular solvate structure tuning3, 4. This helps us to optimize the solubility and redox potential of redox molecule, exemplifying a generic electrolyte design strategy for RFB (see Figure 1). In this presentation, we will discuss the virtual screening and electrolyte design procedures for selecting functional moieties, additive salt and co-solvent to enhances the much-needed redox molecule solubility and stability. References M. Vijayakumar, W. Wang, Z. Nie, V. Sprenkle and J. Hu, Journal of Power Sources 241, 173-177 (2013).M. Vijayakumar, Z. Nie, E. Walter, J. Hu, J. Liu, V. Sprenkle and W. Wang, ChemPlusChem 80 (2), 428-437 (2015).K. S. Han, N. N. Rajput, M. Vijayakumar, X. L. Wei, W. Wang, J. Z. Hu, K. A. Persson and K. T. Mueller, J Phys Chem C 120 (49), 27834-27839 (2016).L. Li, S. Kim, W. Wang, M. Vijayakumar, Z. Nie, B. Chen, J. Zhang, G. Xia, J. Hu, G. Graff, J. Liu and Z. Yang, Advanced Energy Materials 1 (3), 394-400 (2011). Figure 1

A Novel Class of Schistosoma mansoni Histone Deacetylase 8 (HDAC8) Inhibitors Identified by Structure-Based Virtual Screening and In Vitro Testing.

A promising means in the search of new small molecules for the treatment of schistosomiasis (amongst other parasitic ailments) is by targeting the parasitic epigenome. In the present study, a docking based virtual screening procedure using the crystal structure of histone deacetylase 8 from Schistosoma mansoni (smHDAC8) was designed. From the developed screening protocol, we were able to identify eight novel N-(2,5-dioxopyrrolidin-3-yl)-n-alkylhydroxamate derivatives as smHDAC8 inhibitors with IC50 values ranging from 4.4–20.3 µM against smHDAC8. These newly identified inhibitors were further tested against human histone deacetylases (hsHDAC1, 6 and 8), and were found also to be exerting interesting activity against them. In silico prediction of the docking pose of the compounds was confirmed by the resolved crystal structure of one of the identified hits. This confirmed these compounds were able to chelate the catalytic zinc ion in a bidentate fashion, whilst showing an inverted binding mode of the hydroxamate group when compared to the reported smHDAC8/hydroxamates crystal structures. Therefore, they can be considered as new potential scaffold for the development of new smHDAC8 inhibitors by further investigation of their structure–activity relationship.