Ligand-based Virtual Screening Research Articles

A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer

BackgroundColorectal cancer (CRC) is a worldwide cancer with rising annual incidence. New medications for patients with CRC are still needed. Recently, fluorescent chemical probes have been developed for cancer imaging and therapy. Signal transducer and activator of transcription 1 (STAT1) has complex functions in tumorigenesis and its role in CRC still needs further investigation.MethodsRNA sequencing datasets in the NCBI GEO repository were analyzed to investigate the expression of STAT1 in patients with CRC. Xenograft mouse models, tail vein injection mouse models, and azoxymethane/dextran sodium sulfate (AOM/DSS) mouse models were generated to study the roles of STAT1 in CRC. A ligand-based high-throughput virtual screening approach combined with SWEETLEAD chemical database analysis was used to discover new STAT1 inhibitors. A newly designed and synthesized fluorescently labeled 4’,5,7-trihydroxyisoflavone (THIF) probe (BODIPY-THIF) elucidated the mechanistic actions of STAT1 and THIF in vitro and in vivo. Colonosphere formation assay and chick chorioallantoic membrane assay were used to evaluate stemness and angiogenesis, respectively.ResultsUpregulation of STAT1 was observed in patients with CRC and in mouse models of AOM/DSS-induced CRC and metastatic CRC. Knockout of STAT1 in CRC cells reduced tumor growth in vivo. We then combined a high-throughput virtual screening approach and analysis of the SWEETLEAD chemical database and found that THIF, a flavonoid abundant in soybeans, was a novel STAT1 inhibitor. THIF inhibited STAT1 phosphorylation and might bind to the STAT1 SH2 domain, leading to blockade of STAT1-STAT1 dimerization. The results of in vitro and in vivo binding studies of THIF and STAT1 were validated. The pharmacological treatment with BODIPY-THIF or ablation of STAT1 via a CRISPR/Cas9-based strategy abolished stemness and angiogenesis in CRC. Oral administration of BODIPY-THIF attenuated colitis symptoms and tumor growth in the mouse model of AOM/DSS-induced CRC.ConclusionsThis study demonstrates that STAT1 plays an oncogenic role in CRC. BODIPY-THIF is a new chemical probe inhibitor of STAT1 that reduces stemness and angiogenesis in CRC. BODIPY-THIF can be a potential tool for CRC therapy as well as cancer cell imaging.

GBO-kNN a new framework for enhancing the performance of ligand-based virtual screening for drug discovery

Virtual Screening (VS) in drug discovery campaigns deals with enormously large databases containing massive redundant and/or irrelevant features. Thus, the pre-selection process of virtual screening requires developing a fast and precise classification methodology for filtering such a huge database. This paper proposed a framework depending on a wrapper selection approach for features selection. It consists of 1) an optimizer: Gradient-Based Optimizer (GBO), that hybridized with 2) the classifier: k-nearest neighbor (k-NN). The performance of the introduced framework, GBO-kNN, is estimated using real-world benchmark datasets; the QSAR Biodegradation which is composed of 41 features, and the Monoamine Oxidase (MAO) that consisted of 1665 features. the GBO-kNN framework is compared against seven recent swarm intelligence algorithms: Hybrid Harris Hawks Optimization Algorithm (HHO), Grey Wolf Optimization Algorithm (GWO), Butterfly Optimization Algorithm (BOA), Dragonfly Algorithm (DA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), and Salp Swarm Algorithm (SSA) for reaching maximum classification accuracy. The experimental results exhibited that the proposed process, in comparison with other algorithms, achieved high effective accuracy of 98.8% over the high dimensional dataset (MAO) and a moderate effective performance over the low dimensional dataset (QSAR Biodegradation). The proposed framework source code is available at https://codeocean.com/capsule/9906421/tree/v1.

Discovering inhibitors of TEAD palmitate binding pocket through virtual screening and molecular dynamics simulation

Transcriptional enhanced associate domain (TEAD) proteins bind to YAP/TAZ and mediate YAP/TAZ-induced gene expression. TEADs are not only the key transcription factors and final effector of the Hippo signaling pathway, but also the proteins that regulate cell proliferation and apoptosis. Disorders of Hippo signaling pathway occur in liver cancer, breast cancer, colon cancer and other cancers. S-palmitylation can stabilize the structure of TEADs and is also a necessary condition for the binding of TEADs to YAP/TAZ. The absence of TEAD palmitoylation prevents TEADs from binding to chromatin, thereby inhibiting the transcription and expression of downstream target genes in the Hippo pathway through a dominant-negative mechanism. Therefore, disrupting the S-palmitylation of TEADs has become an attractive and very feasible method in cancer treatment. The palmitate binding pockets of TEADs are conservative, and the crystal structures of TEAD2-palmitoylation inhibitor complexes and the potential TEAD2 inhibitors are more than other TEADs, TEAD2 can be selected to be the target receptor. In this study, structure-based and ligand-based virtual screening, molecular dynamics simulations, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) calculations, residue decomposition binding energy calculations, and ADME predictions have been performed to discover 11 potential TEAD2 S-palmitylation inhibitors. ChEBML196567 and ZINC000013942794 are the most recommended, because they formed strong binding energies and stable hydrogen bonds with TEAD2 and have good drugbility and high human oral absorption. We found that it was easier to find the targeting small molecules using a combination of structure-based and ligand-based virtual screening methods. Besides, a new core structure has been found in the selected small molecules. In addition, we analyzed the binding modes of these small molecules to TEAD2, and confirmed the hot spot residues Cys380, Ser345, Tyr426, Phe428, Ile408, and Met379. Availability of data and materialSupplementary materials are available online.

Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation

In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model ‘Hypo 1’ showed excellent correlation (R2 = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R2 = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn2+ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.

Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity.

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.

Ligand Based Virtual Screening Using Self-organizing Maps.

Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN.

The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience.

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

The Natural Ligand for Metalloproteinase-A Multifaceted Drug Target.

Metalloproteinase is one of the key components of Russell viper venom and it is the root cause of edema, blood coagulation, local tissue damage, hemorrhage, and inflammation during snakebite envenoming. Hence, finding a suitable metalloproteinase inhibitor from natural source will be of great biological importance in mitigating pathological effects. In this current study, we employed computational analysis to examine the inhibition of metalloproteinase by phytochemicals present in Andrographis paniculata. Molecular docking studies revealed interaction of A. paniculata phytochemicals with the catalytic M domain's active site amino acid residues, namely ASN203, ARG293, PHE203, LEU206, LYS199, and ALA122, similar to that of the reference compound Batimastat. 14-acetylandrographolide, 14-deoxy-11,12 didehydroandrographolide, Andrograpanin, Isoandrographolide, and 14-deoxy-11-oxoandrographolide displayed high binding energy and inhibition against the metalloproteinase. Molecular dynamic simulation analysis revealed less root mean square fluctuation of amino acid residues of metalloproteinase-14-acetylandrographolide complex than metalloproteinase-Batimastat complex indicating the high stability for metalloproteinase with the phytochemical. In silico analysis of parameters like ADME properties and drug-likeness of the phytochemicals exhibited good pharmacokinetic properties. Ligand-based virtual screening of phytochemicals to identify similarity to FDA-approved drugs and identification of their possible targets were also performed. The outcome of the current study strengthens the significance of these phytochemicals as promising lead candidates for the treatment of snakebite envenomation. Moreover, the study also encourages the in vivo and in vitro evaluation of the phytochemicals to validate the computational findings.

Sidastrum paniculatum (L.) Fryxell (Malvaceae): A Promising Source of Bioactive Sulfated Flavonoids Against Aedes aegypti L

Aedes aegypti L. is known as the most relevant vector mosquito for viruses such as yellow fever, chikungunya, dengue, and Zika, especially in places with unplanned urbanization, and erratic water supply. Plants used in folk medicine have become a useful source of active compounds with the potential to control the dissemination of Ae. aegypti. Compounds isolated from Malvaceae sensu lato have been previously reported as larvicides, repellents, and insecticides. Recent studies have demonstrated the anti Ae. aegypti activity of sulfated flavonoids, an uncommon type of flavonoid derivatives. This research reports the phytochemical investigation of Sidastrum paniculatum (L.) Fryxell, a Malvaceae species with the potential against Ae. aegypti. Chromatographic procedures resulted in the isolation of the compounds: stearic acid (1), N-trans-feruloyltyramine (2), acacetin (3), apigenin (4), tiliroside (5), along with the sulfated flavonoids: wissadulin (6), 7,4′-di-O-methyl-8-O-sulfate flavone (7), yannin (8), beltraonin (9a), 7-O-sulfate isoscutellarein (paniculatumin) (9b), and condadin (10). This is the first report of compound 7-O-sulfate isoscutellarein (9b). The structures were elucidated by spectroscopic analysis (NMR, LC-HRMS and FT-IR). The sulfated flavonoids identified were submitted to a ligand-based and structure-based virtual screening against two targets: 1YIY (from adult Ae. aegypti) and 1PZ4 (from Ae. aegypti larvae). The results indicated that when the O-sulfate group is bearing the position 7, the structures are potentially active in 1PZ4 protein. On the other hand, flavonoids with the O-sulfate group bearing position 8 were showed to be more likely to bind to the 1YIY protein. Our findings indicated that S. paniculatum is a promising source of sulfated flavonoids with potential against Ae. aegypti.

Ligand-based drug repurposing strategy identified SARS-CoV-2 RNA G-quadruplex binders.

The single-stranded RNA genome of SARS-CoV-2 contains some G-quadruplex-forming G-rich elements which are putative drug targets. Here, we performed a ligand-based pharmacophore virtual screening of FDA approved drugs to find candidates targeting such RNA structures. Further in silico and in vitro assays identified three drugs as emerging SARS-CoV-2 RNA G-quadruplex binders.

Seawater fungi-derived compound screening to identify novel small molecules against dengue virus NS5 methyltransferase and NS2B/NS3 protease

Dengue fever is a virus spread by mosquitoes that has no effective treatment or vaccination. Several dengue cases combined with the current COVID-19 pandemic, exacerbates this problem. Two proteins, NS5 methyltransferase and NS2B/NS3 primary protease complexes, are crucial for dengue viral replication and are the target sites for antiviral development. Thus, this study screened published literature and identified 162 marine fungus-derived compounds with active bioavailability. Following Lipinski's rules and antiviral property prediction, 41 compounds were selected for docking with NS5 methyltransferase and NS2B/NS3 protease (PDB ID: 6IZZ and 2FOM) to evaluate compounds that could stop the action of dengue viral protein complexes. To find the best candidates, computational ADME, toxicity, and drug target prediction were performed to estimate the potential of the multi-targeting fungal-derived natural compounds. Analyzing the result from 41 compounds, Chevalone E (−13.5 kcal/mol), Sterolic acid (−10.3 kcal/mol) showed higher binding energy against dengue NS2B/NS3 protease; meanwhile, Chevalone E (−12.0 kcal/mol), Brevione K (−7.4 kcal/mol), had greater binding affinity against NS5 methyltransferase. Consequently, this study suggests that Chevalone E is an effective inhibitor of NS5 methyltransferase and NS2B/NS3 protease. Ligand-based virtual screening from DrugBank was utilized to predict biologically active small compounds against dengue virus NS2B/NS3 major protease and NS5 methyltransferase. Both licensed medications, estramustine (DB01196) and quinestrol (DB04575), were found to be similar to Chevalone E, with prediction scores of 0.818 and 0.856, respectively. In addition, cholic acid (DB02659), acitretin (DB00459), and mupirocin (DB00410) are similar to Sterolic acid, zidovudine (DB00495), imipenem (DB01598), and nadolol (DB01203) are similar to Brocazine A, and budesonide (DB01222) and colchicine (DB01394) are related to Brevione K. These findings suggest that these could be feasible dengue virus treatment options, meaning that more research is needed.

Discovery of novel drug-like antitubercular hits targeting the MEP pathway enzyme DXPS by strategic application of ligand-based virtual screening.

In the present manuscript, we describe how we successfully used ligand-based virtual screening (LBVS) to identify two small-molecule, drug-like hit classes with excellent ADMET profiles against the difficult to address microbial enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS). In the fight against antimicrobial resistance (AMR), it has become increasingly important to address novel targets such as DXPS, the first enzyme of the 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway, which affords the universal isoprenoid precursors. This pathway is absent in humans but essential for pathogens such as Mycobacterium tuberculosis, making it a rich source of drug targets for the development of novel anti-infectives. Standard computer-aided drug-design tools, frequently applied in other areas of drug development, often fail for targets with large, hydrophilic binding sites such as DXPS. Therefore, we introduce the concept of pseudo-inhibitors, combining the benefits of pseudo-ligands (defining a pharmacophore) and pseudo-receptors (defining anchor points in the binding site), for providing the basis to perform a LBVS against M. tuberculosis DXPS. Starting from a diverse set of reference ligands showing weak inhibition of the orthologue from Deinococcus radiodurans DXPS, we identified three structurally unrelated classes with promising in vitro (against M. tuberculosis DXPS) and whole-cell activity including extensively drug-resistant strains of M. tuberculosis. The hits were validated to be specific inhibitors of DXPS and to have a unique mechanism of inhibition. Furthermore, two of the hits have a balanced profile in terms of metabolic and plasma stability and display a low frequency of resistance development, making them ideal starting points for hit-to-lead optimization of antibiotics with an unprecedented mode of action.

Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches.

The epidermal growth factor receptor (EGFR) signaling pathway plays an important role in cell growth, proliferation, differentiation, and other physiological processes, which makes the EGFR a promising target for anticancer therapies. The discovery of novel EGFR inhibitors may provide a solution to the problem of drug resistance. In this work, we performed a ligand-based virtual screening (LBVS) protocol for finding novel EGFR inhibitors from a 5.3 million compound library. First, the 3D shape-based similarity was used to obtain structurally novel EGFR inhibitors. In this study, we tried three queries; two were crystal structures and one was generated from deep generative models of graphs (DGMG). Next, we have built four structure-activity relationship (SAR) models and three quantitative structure-activity relationship (QSAR) models based on an SVM method for further screening of highly active EGFR inhibitors. Experimental validations led to the identification of nine hits out of 18 tested compounds. Among them, hit 1, hit 5, and hit 6 had IC50 values around 80 nM against EGFR whose interactions with EGFR were further investigated by molecular dynamics simulations.

Identifying the analogues of berberine as promising antitubercular drugs targeting Mtb-FtsZ polymerisation through ligand-based virtual screening and molecular dynamics simulations.

Berberine, an active compound in the extract of golden seal (an age-long remedy for many infections) has been confirmed to be responsible for the extract's activity against multi-drug resistant strain of Mycobacterium tuberculosis. There is no available study that shows the exact target of berberine in M tuberculosis, although it is confirmed that berberine inhibits the polymerisation of filamentous temperature-sensitive mutant Z (FtsZ), an important bacteria cytokinesis protein, in Escherichia coli, suggesting that FtsZ could as well be the target of berberine in M tuberculosis. In this study, we carried out ligand-based virtual screening to identify analogues of berberine followed by molecular dynamics (MD) simulations of the complexes of Mtb-FtsZ with berberine (berb1) and the five selected analogues (berb9 [ZINC1709414], berb37 [ZINC238749993], berb38 [ZINC13509022], berb43 [ZINC14765594], and berb48 [ZINC238758595]). Post-MD analyses such as binding free energy, RMSD, RMSF, RoG and hydrogen bond lifetime analysis were used to understand the interactions between these ligands and the receptor. The results suggested that Mtb-FtsZ could likely be the target of berberine in M tuberculosis as it forms a stable complex coupled with a significantly high binding energy. The study also identified other potential inhibitors of MTB-FtsZ polymerisation. Berb38 specifically showed greater interaction with the residues at the binding site of the protein, forming a far more stable complex with the receptor than any of the other compounds under investigation, including berberine itself. ADME properties calculations also predicted all the ligands to be bioactive as orally administered drugs.

Ligand-Based Virtual Screening Based on the Graph Edit Distance.

Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets—CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS—have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.

Virtual Screening-Based Identification of Potent DENV-3 RdRp Protease Inhibitors via In-House Usnic Acid Derivative Database

Dengue virus (DENV) is the causative agent of dengue fever, dengue hemorrhagic disease and dengue shock syndrome (DSS), transmitted predominantly in tropical and subtropical regions by Aedes aegypti. It infects millions of people and causes thousands of deaths each year, but there is no antiviral drug against DENV. Usnic acid lately piqued the interest of researchers for extraordinary biological characteristics, including antiviral activity. Based on high larvicidal activities against Aedes aegypti, this study aims to search usnic acid derivatives as novel anti-DENV agents through a combination of ligand-based and pharmacophore-based virtual screening. One hundred and sixteen (116) usnic acid derivatives were obtained from a database of 428 in-house usnic acid derivatives through pharmacophore filtering steps. Subsequent docking simulation on DENV-3 NS-5 RdRp afforded 41 compounds with a strong binding affinity towards the enzyme. The pharmacokinetics and drug likeness prediction resulted in seven hit compounds, which eventually undergo cytochrome P450 enzyme screening to obtain the lead compound, labelled as 362. In addition, molecular dynamic (MD) simulation of lead compound 362 was performed to verify the stability of the docked complex and the binding posture acquired in docking experiments. Overall, the lead compounds have shown a high fit value of pharmacophore, strong binding affinity towards RdRp enzyme, good pharmacokinetics, and drug likeness properties. The discovery of a new usnic acid derivative as a novel anti-DENV agent targeting RdRp could lead to further drug development and optimization to treat dengue.

Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods.

Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.

Identification of potential inhibitory analogs of metastasis tumor antigens (MTAs) using bioactive compounds: revealing therapeutic option to prevent malignancy.

The deeper understanding of metastasis phenomenon and detection of drug targets could be a potential approach to minimize cancer mortality. In this study, attempts were taken to unmask novel therapeutics to prevent metastasis and cancer progression. Initially, we explored the physiochemical, structural and functional insights of three metastasis tumor antigens (MTAs) and evaluated some plant-based bioactive compounds as potent MTA inhibitors. From 50 plant metabolites screened, isoflavone, gingerol, citronellal and asiatic acid showed maximum binding affinity with all three MTA proteins. The ADME analysis detected no undesirable toxicity that could reduce the drug likeness properties of top plant metabolites. Moreover, molecular dynamics studies revealed that the complexes were stable and showed minimum fluctuation at molecular level. We further performed ligand-based virtual screening to identify similar drug molecules using a large collection of 376,342 compounds from DrugBank. The results suggested that several structural analogs (e.g., tramadol, nabumetone, DGLA and hydrocortisone) may act as agonist to block the MTA proteins and inhibit cancer progression at early stage. The study could be useful to develop effective medications against cancer metastasis in future. Due to encouraging results, we highly recommend further in vitro and in vivo trials for the experimental validation of the findings.

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases.

Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.