Ligand-based Virtual Screening Research Articles

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach.

The influenza A virus nonstructural protein 1 (NS1), which is crucial for viral replication and immune evasion, has been identified as a significant drug target with substantial potential to contribute to the fight against influenza. The emergence of drug-resistant influenza A virus strains highlights the urgent need for novel therapeutics. This study proposes a combined theoretical criterion for the virtual screening of molecular libraries to identify candidate NS1 inhibitors. By applying the criterion to the ZINC Natural Product database, followed by ligand-based virtual screening and molecular docking, we proposed the most promising candidate as a potential NS1 inhibitor. Subsequently, the selected natural compound was experimentally evaluated, revealing measurable virus replication inhibition activity in cell culture. This approach offers a promising avenue for developing novel anti-influenza agents targeting the NS1 protein.

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation.

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.

Structural insights into the potential binding sites of Cathepsin D using molecular modelling techniques

Alzheimer’s disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

On the relevance of query definition in the performance of 3D ligand-based virtual screening

Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.Graphical

Identifying and characterising promising small molecule inhibitors of kinesin spindle protein using ligand-based virtual screening, molecular docking, molecular dynamics and MM‑GBSA calculations

The kinesin spindle protein (Eg5) is a mitotic protein that plays an essential role in the formation of the bipolar spindles during the mitotic phase. Eg5 protein controls the segregation of the chromosomes in mitosis which renders it a vital target for cancer treatment. In this study our approach to identifying novel scaffold for Eg5 inhibitors is based on targeting the novel allosteric pocket (α4/α6/L11). Extensive computational techniques were applied using ligand-based virtual screening and molecular docking by two approaches, MOE and AutoDock, to screen a library of commercial compounds. We identified compound 8-(3-(1H-imidazol-1-ylpropylamino)-3-methyl-7-((naphthalen-3-yl)methyl)-1H-purine-2, 6 (3H,7H)-dione (compound 5) as a novel scaffold for Eg5 inhibitors. This compound inhibited cancer cell Eg5 ATPase at 2.37 ± 0.15 µM. The molecular dynamics simulations revealed that the identified compound formed stable interactions in the allosteric pocket (α4/α6/L11) of the receptor, indicating its potential as a novel Eg5 inhibitor.Graphical

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.Graphical abstractSpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut.

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death. To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1). Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening. The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

Discovery of novel covalent inhibitors of DJ-1 through hybrid virtual screening.

Background: DJ-1 is a ubiquitously expressed protein with multiple functions. Its overexpression has been associated with the occurrence of several cancers, positioning DJ-1 as a promising therapeutic target forcancer treatment. Methods: To find novel inhibitors of DJ-1, we employed a hybrid virtual screening strategy that combines structure-based and ligand-based virtual screening on a comprehensive compound library. Results: In silico study identified six hit compounds as potential DJ-1 inhibitors that were assessed in vitro at the cellular level. Compound 797780-71-3 exhibited antiproliferation activity in ACHN cells with an IC50 value of 12.18μM and was able to inhibit the Wnt signaling pathway. This study discovers a novel covalent inhibitor for DJ-1 and paves the way for further optimization.

Mitoxantrone and abacavir: An ALK protein-targeted in silico proposal for the treatment of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a type of lung cancer associated with translocation of the EML4 and ALK genes on the short arm of chromosome 2. This leads to the development of an aberrant protein kinase with a deregulated catalytic domain, the cdALK+. Currently, different ALK inhibitors (iALKs) have been proposed to treat ALK+ NSCLC patients. However, the recent resistance to iALKs stimulates the exploration of new iALKs for NSCLC. Here, we describe an in silico approach to finding FDA-approved drugs that can be used by pharmacological repositioning as iALK. We used homology modelling to obtain a structural model of cdALK+ protein and then performed molecular docking and molecular dynamics of the complex cdALK+-iALKs to generate the pharmacophore model. The pharmacophore was used to identify potential iALKs from FDA-approved drugs library by ligand-based virtual screening. Four pharmacophores with different atomistic characteristics were generated, resulting in six drugs that satisfied the proposed atomistic positions and coupled at the ATP-binding site. Mitoxantrone, riboflavin and abacavir exhibit the best interaction energies with 228.29, 165.40 and 133.48 KJoul/mol respectively. In addition, the special literature proposed these drugs for other types of diseases due to pharmacological repositioning. This study proposes FDA-approved drugs with ALK inhibitory characteristics. Moreover, we identified pharmacophores sites that can be tested with other pharmacological libraries.

Riemannian geometry and molecular similarity I: spectrum of the Laplacian

Ligand-based virtual screening aims to reduce the cost and duration of small molecule drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing with molecules with known favourable properties. This paper presents the theory underpinning RGMolSA, a new alignment-free and mesh-free surface-based molecular shape descriptor derived from the mathematical theory of Riemannian geometry. The treatment of a molecule as a series of intersecting spheres allows the description of its surface geometry using the Riemannian metric , obtained by considering the spectrum of the Laplacian. This gives a simple vector descriptor constructed of the weighted surface area and eight non-zero eigenvalues, which capture the surface shape. We demonstrate the potential of our method by considering a series of PDE5 inhibitors that are known to have similar shape as an initial test case. RGMolSA displays promise when compared with existing shape descriptors and in its capability to handle different molecular conformers.

In silico and structural investigation of sulfonamides targeting VraSR two component system in methicillin-resistant Staphylococcus aureus

Drug-resistant Staphylococcus aureus strains are global health concerns. Several studies have shown that these strains can develop defences against cell wall antibiotics such as β-lactams, glycopeptides and daptomycin which target cell wall biosynthesis. The coordination of these responses have been associated with two component system (TCS) regulated by histidine kinase protein (VraS) and its cognate regulator VraR which influences the target DNA upon signal recognition. Computer-based screening methods, predictions and simulations have emerged as more efficient and quick ways to identify promising new compound leads from large databases against emerging drug targets thus allowing prediction of small select set of molecules for further validations. These combined approaches conserve valuable time and resources. Due to methicillin resistance, sulfonamide-derivative medications have been found to be effective treatment strategy to treat S. aureus infections. The current study used ligand-based virtual screening (LBVS) to identify powerful sulfonamide derivative inhibitors from an antibacterial compound library against VraSR signaling components, VraS and VraR. We identified promising sulfonamide derivative [compound 5: (4-[(1-{[(3,5-Dimethoxyphenyl)Carbamoyl]Methyl}-2,4-Dioxo-1,2,3,4-Tetrahydroquinazolin-3-Yl)Methyl]-N-[(Furan-2-Yl)Methyl]Benzamide)] with reasonable binding parameters of −31.38 kJ/mol and ΔGbind score of −294.32 kJ/mol against ATP binding domain of sensor kinase VraS. We further identified four compounds N1 (PCID83276726), N3 (PCID83276757), N9 (PCID3672584), and N10 (PCID20900589) against VraR DNA binding domain (VraRC) with ΔGbind energies of −190.27, −237.54, −165.21, and −190.88 kJ/mol, respectively. Structural and simulation analyses further suggest their stable interactions with DNA interacting residues and potential to disrupt DNA binding domain dimerization; therefore, it is prudent to further investigate and characterize them as VraR dimer disruptors and inhibit other promoter binding site. Interestingly, the discovery of drugs that target VraS and VraR may open new therapeutic avenues for drug-resistant S. aureus. These predictions based on screening, simulations and binding affinities against VraSR components hold promise for opening novel therapeutic avenues against drug-resistant S. aureus and present opportunities for repositioning efforts. These efforts aim to create analogs with enhanced potency and selectivity against two-component signaling systems that significantly contribute to virulence in MRSA or VRSA. These analyses contribute valuable insights into potential avenues for combating antibiotic-resistant S. aureus through computationally driven drug discovery strategies. Communicated by Ramaswamy H. Sarma

S-Dihydrodaidzein and 3-(1,3-benzoxazol-2-yl)-benzamide, Two New Potential β-estrogen Receptor Ligands with Anti-adipogenic Activity.

The elucidation of molecular pathways associated with adipogenesis has evidenced the relevance of estrogen and estrogen receptor beta (ERβ). The positive effects of ERβ ligands on adipogenesis, energy expenditure, lipolysis, food intake, and weight loss, make ERβ an attractive target for obesity control. From ligand-based virtual screening, molecular docking, and molecular dynamic simulations, six new likely ERβ ligands (C1 to C6) have been reported with potential for pharmacological obesity treatment. In this study, the effect of molecules C1-C6 on adipogenesis using the murine 3T3-L1 cell line was evaluated. Cell viability was assessed by MTT assays. Lipid accumulation and gene expression were investigated by ORO staining and real-time quantitative RT-PCR experiments, respectively. Cell viability was not significantly affected by C1-C6 at concentrations up to 10 μM. Interestingly, treatment with 10 μM of C1 (S-Dihydrodaidzein) and C2 (3-(1,3-benzoxazol-2-yl)- benzamide) for 72 h inhibited adipocyte differentiation; moreover, ORO staining evidenced a reduced intracellular lipid accumulation (40% at day 7). Consistently, mRNA expression of the adipogenic markers, PPARγ and C/EBPα, was reduced by 50% and 82%, respectively, in the case of C1, and by 83% and 59%, in the case of C2. Altogether, these results show the two new potential β-estrogen receptor ligands, C1 and C2, to exhibit anti-adipogenic activity. They could further be used as lead structures for the development of more efficient drugs for obesity control.

LIGAND-BASED VIRTUAL SCREENING OF FDA-APPROVED DRUGS TO IDENTIFY NEW INHIBITORS AGAINST LACTATE DEHYDROGENASE ENZYME OF MALARIA PARASITES

Objective: The aim of this study is to computationally repurpose FDA-approved drugs as potential inhibitors of the Plasmodium falciparum lactate dehydrogenase (PfLDH) by competing with the cofactor NADH. Methods: In this in silico study, we have virtually screened a library of FDA-approved drugs for structural similarity to the dihydro nicotinamide adenine dinucleotide (NADH). Then, the top hits were further assessed for clinical safety and by application of molecular docking and dynamics simulation. Results: Ligand-based virtual screening reports that the antibiotic Novobiocin has a good similarity to the cofactor NADH with a score of 0.7. Also, molecular docking study indicates that Novobiocin may has the ability to interact with PfLDH enzyme with a docking energy of-8.8 Kcal/mol. However, during molecular dynamics (MD) simulation, the mean ligand proximity root mean square deviation (RMSD) and binding energy for Novobiocin were 4.3 Angstrom and-37.45 Kcal/mol, respectively. These MD simulation parameters are inferior to those recorded for NADH molecule during 50 nanosecond intervals. Conclusion: The antibiotic Novobiocin may serve as a potential lead candidate toward the design of novel antimalarial agents. However, further evaluation of Novobiocin may be recommended to affirm its capacity against PfLDH enzyme.

Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv

ABSTRACT Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

Design and Discovery of Silmitasertib-based Drugs as a Potential Casein Kinase II Inhibitor for Cholangiocarcinoma through Hybrid In-silico Ligand-Based Virtual Screening with Molecular Docking Method

This research investigates the design and discovery of silmitasertib-based drugs as potential inhibitors of casein kinase II (CK2) for cholangiocarcinoma using hybrid in-silico methods. The study employs a two-fold approach, including ligand-based virtual screening and molecular docking, to assess the inhibitory potential of silmitasertib-based compounds. The ligand-based virtual screening involves the construction of a diverse compound library, followed by energy minimization and conformational analysis, revealing a set of compounds with varied similarities to silmitasertib. In parallel, the CK2 model undergoes meticulous selection, validation, and refinement through PDB REDO, showcasing significant improvements in model quality. Molecular docking results highlight promising candidates, with N-(2, 4, 6-trimethylphenyl) phenanthridin-6-amine (MCULE-1492185963-0) emerging as a lead compound with superior binding characteristics compared to silmitasertib. This hybrid in-silico approach demonstrates the potential of silmitasertib-based compounds as CK2 inhibitors for cholangiocarcinoma and identifies a lead compound for further experimental validation. The findings contribute valuable insights to the design of novel drugs for cholangiocarcinoma treatment, setting the stage for future experimental and clinical investigations

Catechins as Catalase Modulators: A Comprehensive In-silico Analysis Unveiling their Potential Antioxidant Effects

This research explores the potential of carefully selected catechins as catalase modulators, leveraging their documented antioxidant effects. The catalase protein’s structural quality was rigorously evaluated, revealing an overall high-quality 3D structure. Ligand-based virtual screening identified ten novel catechin hits with promising interactions, presenting candidates designed for new validation. Molecular docking simulations demonstrated robust binding empathies between selected catechins and catalase, with CHEMBL223855 exhibiting the highest affinity. ADMET analysis highlighted CHEMBL223855 as a promising candidate for drug development, boasting favorable properties, including high gastrointestinal absorption and absence of blood-brain barrier permeation. Despite medicinal chemistry alerts and lead likeness violations, this comprehensive analysis guides future experimental validation efforts, supporting the potential of these catechins as effective catalase modulators.

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment. Communicated by Ramaswamy H. Sarma

Scipion-Chem: An Open Platform for Virtual Drug Screening.

Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.