ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery

In-silico prediction, characterization, molecular docking and dynamic simulation studies for screening potential fungicides against leaf rust of Triticum aestivum

Triticum aestivum is an important crop worldwide, which is a large source of food grain. T.aestivum demands on developed countries will grow every year, this increase in the demand is profoundly serious especially in the light climate change which would lead to a 29% reduction in final productivity. Rust fungus attacks the T.aestivum, specifically newly planted T.aestivum plants, which block the vascular system, stun, and finally damage grain and tillers. In present study we predict the 3D structure then find the binding pocket and conserved domains for MAPkinase-1 of Puccinia triticina. After that, screen the FungiPAD, PubChem, NPAtlas databases by physicochemical properties, docking, clustering, ADME (Absorption, distribution, metabolism, and excretion) and PAINS (pan assay interference compounds) filter analysis. Through this screening process screen the nine compounds, which are benzovindiflupyr, furametpyr, isopyrazam, fenaminstrobin, and flumorph from Fungicide database: zoxamide, vinclozolin, pentachloronitrobenzene, and dithianon from PubChem database, based on the binding energy, clustering, ADME and PAINS analysis. All these nine compounds bind in the same pocket and show the same pattern of interaction. Among these nine compounds, select the two compounds (PubChem:122087 (-6.96 kcal/mol) and FDBD02904 (-8.62 kcal/mol)) based on binding energy for 100 ns MD simulation and free energy calculation. MD simulation shows stability throughout the simulation, and it shows the sable interaction when compounds bind to the MAPKinase 1 protein which may help to protein kinase pathways in plant defense response. This result helps to design alternative fungicide against the wheat rust disease.Communicated by Ramaswamy H. Sarma.

Tadalafil Rescues the p.M325T Mutant of Best1 Chloride Channel

Bestrophin 1 (Best1) is a chloride channel that localises to the plasma membrane of retinal pigment epithelium (RPE) cells. Mutations in the BEST1 gene are associated with a group of untreatable inherited retinal dystrophies (IRDs) called bestrophinopathies, caused by protein instability and loss-of-function of the Best1 protein. 4PBA and 2-NOAA have been shown to rescue the function, expression, and localisation of Best1 mutants; however, it is of interest to find more potent analogues as the concentration of the drugs required is too high (2.5 mM) to be given therapeutically. A virtual docking model of the COPII Sec24a site, where 4PBA has been shown to bind, was generated and a library of 1416 FDA-approved compounds was screened at the site. The top binding compounds were tested in vitro in whole-cell patch-clamp experiments of HEK293T cells expressing mutant Best1. The application of 25 μM tadalafil resulted in full rescue of Cl^- conductance, comparable to wild type Best1 levels, for p.M325T mutant Best1 but not for p.R141H or p.L234V mutants.

Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification

Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient's is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 -an orphan G protein-coupled receptor- is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs -ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)- as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC₅₀ of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)- silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.

Drug Repurposing for Identification of S1P1 Agonists with Potential Application in Multiple Sclerosis Using In Silico Drug Design Approaches

Purpose: Drug repurposing is an approach successfully used for discovery of new therapeutic applications for the existing drugs. The current study was aimed to use the combination of in silico methods to identify FDA-approved drugs with possible S1P₁ agonistic activity useful in multiple sclerosis (MS). Methods: For this, a 3D-QSAR model for the known 21 S1P₁ agonists were generated based on 3D-QSAR approach and used to predict the possible S1P₁ agonistic activity of FDA-approved drugs. Then, the selected compounds were screened by docking into S1P₁ and S1P₃ receptors to select the S1P₁ potent and selective compounds. Further evaluation was carried out by molecular dynamics (MD) simulation studies where the S1P₁ binding energies of selected compounds were calculated. Results: The analyses resulted in identification of cobicistat, benzonatate and brigatinib as the selective and potent S1P₁ agonists with the binding energies of -85.93, -69.77 and -67.44 kcal. mol^-1, calculated using MM-GBSA algorithm based on 50 ns MD simulation trajectories. These values are better than that of siponimod (-59.35 kcal mol^-1), an FDA approved S1P₁ agonist indicated for MS treatment. Furthermore, similarity network analysis revealed that cobicistat and brigatinib are the most structurally favorable compounds to interact with S1P₁. Conclusion: The findings in this study revealed that cobicistat and brigatinib can be evaluated in experimental studies as potential S1P₁ agonist candidates useful in the treatment of MS.

In-silico screening and identification of phytochemicals from Centella asiatica as potential inhibitors of sodium-glucose co-transporter 2 for treating diabetes

Sodium-glucose co-transporter 2 (SGLT-2) is a major transport protein responsible for reabsorption of glucose from the kidney back to the bloodstream. Inhibiting this protein effectively lowers the glucose level of diabetic patients; however, the use of synthetic SGLT-2 inhibitors has been linked to some serious adverse effects. There is a need to identify safer alternatives that are equally or more effective as the current inhibitor drugs. Phytochemicals are known for their efficacy as herbal remedies, but these molecules remain underexplored as source of therapeutic agents. In this study, we performed in silico screening to identify potential SGLT-2 inhibitors from the 21 phytochemicals from Centella asiatica. Docking results identified eleven compounds with estimated binding energies comparable to that of known inhibitors drugs. The stability of the complexes was then elucidated using 100 ns MD simulations. From our dynamic binding free energy calculations using MM/PBSA, asiaticoside, betulinic acid, centellasapogenol, methyl brahmate, and rutin exceeded at least one of the binding energies of the reference compounds, which highlights their strong affinity towards SGLT-2. Among the five, betulinic acid, centellasapogenol, and methyl brahmate maintained their structural stability to the same extent as the references and exhibited better oral bioavailability and excellent drug-like properties. Because of these results, it is recommended to prioritize betulinic acid, centellasapogenol, and methyl brahmate in future in vitro and in vivo studies to verify their potential as inhibitor drugs for diabetes therapies. Communicated by Ramaswamy H. Sarma.

Flavan-based phytoconstituents inhibit Mpro, a SARS-COV-2 molecular target, in silico

A well-validated in-silico approach can provide promising drug candidates for the treatment of the ongoing CoVID19 pandemic. In this study, we have screened 32 phytochemical constituents (PCCs) with Mpro binding site (PDB:6W63) based on which we identified three possible candidates that are likely to be effective against CoVID19-viz., licoleafol (binding energy: -8.1 kcal/mol), epicatechin gallate (-8.5 kcal/mol) and silibinin (-8.4 kcal/mol) that result in higher binding affinity than the known inhibitor, X77 (-7.7 kcal/mol). Molecular dynamics (MD) simulations of PCCs-Mpro complex confirmed molecular docking results with high structural and dynamical stability. The selected compounds were found to exhibit low mean squared displacements (licoleafol: 2.25 ± 0.43 Å, epicatechin gallate: 1.93 ± 0.35 Å, and silibinin: 1.39 ± 0.19 Å) and overall low fluctuations of the binding complexes (root mean squared fluctuations below 2 Å). Visualization of the MD trajectories and structural analyses revealed that they remain confined to the initial binding region, with mean fluctuations lower than 3 Å. To access the collective motion of the atoms, we performed principal component analysis demonstrating that the first 10 principal components are the major contributors (approximate contribution of 80%) and are responsible for the overall PCCs motion. Considering that the three selected PCCs share the same flavan backbone and exhibit antiviral activity against hepatitis C, we opine that licoleafol, epi-catechin gallate, and silibinin can be promising anti-CoVID19 drug candidates. Communicated by Ramaswamy H. Sarma.

Identification of FDA-approved drugs against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) through computational virtual screening

The SARS-CoV-2 coronavirus is responsible for the COVID-19 outbreak, which overwhelmed millions of people worldwide; hence, there is an urgency to identify appropriate antiviral drugs. This study focuses on screening compounds that inhibit RNA-dependent RNA-polymerase (RdRp) essential for RNA synthesis required for replication of positive-strand RNA viruses. Computational screening against RdRp using Food and Drug Administration (FDA)-approved drugs identified ten prominent compounds with binding energies of more than - 10.00 kcal/mol, each a potential inhibitor of RdRp. These compounds' binding energy is comparable to known RdRp inhibitors remdesivir (IC50 = 10.09 μM, SI = 4.96) and molnupiravir (EC50 = 0.67 - 2.66 µM) and 0.32-2.03 µM). Remdesivir and molnupiravir have been tested in clinical trial and remain authorized for emergency use in the treatment of COVID-19. In docking simulations, selected compounds are bound to the substrate-binding pocket of RdRp and showed hydrophobic and hydrogen bond interaction. For molecular dynamics simulation, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate were selected from the initial ten candidate compounds. MD simulation indicated that these compounds are stable at 50-ns MD simulation when bound to RdRp protein. The screen hit compounds, remdesivir, molnupiravir, and GS-441524, are bound in the substrate binding pocket with good binding-free energy. As a consequence, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate are potential new inhibitors of RdRp protein with potential of limiting COVID-19 infection by blocking RNA synthesis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02072-1.

Recent Advances of DprE1 Inhibitors against Mycobacterium tuberculosis: Computational Analysis of Physicochemical and ADMET Properties

Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) is a critical flavoenzyme in Mycobacterium tuberculosis, catalyzing a vital step in the production of lipoarabinomannan and arabinogalactan, both of which are essential for cell wall biosynthesis. Due to its periplasmic localization, DprE1 is a susceptible target, and several compounds with diverse scaffolds have been discovered that inhibit this enzyme, covalently or noncovalently. We evaluated a total of ∼1519 DprE1 inhibitors disclosed in the literature from 2009 to April 2022 by performing an in-depth analysis of physicochemical descriptors and absorption, distribution, metabolism, excretion, and toxicity (ADMET), to gain new insights into these properties in DprE1 inhibitors. Several molecular properties that should facilitate the design and optimization of future DprE1 inhibitors are described, allowing for the development of improved analogues targeting M. tuberculosis.

Identification of CB1 Ligands among Drugs, Phytochemicals and Natural-Like Compounds: Virtual Screening and In Vitro Verification

Cannabinoid receptor type 1 (CB1) is an important modulator of many key physiological functions and thus a compelling molecular target. However, safe CB1 targeting is a non-trivial task. In recent years, there has been a surge of data indicating that drugs successfully used in the clinic for years (e.g. paracetamol) show CB1 activity. Moreover, there is a lot of promise in finding CB1 ligands in plants other than Cannabis sativa. In this study, we searched for possible CB1 activity among already existing drugs, their metabolites, phytochemicals, and natural-like molecules. We conducted two iterations of virtual screening, verifying the results with in vitro binding and functional assays. The in silico procedure consisted of a wide range of structure- and ligand-based methods, including docking, molecular dynamics, and quantitative structure-activity relationship (QSAR). As a result, we identified travoprost and ginkgetin as CB1 ligands, which provides a starting point for future research on the impact of their metabolites or preparations on the endocannabinoid system. Moreover, we found five natural-like compounds with submicromolar or low micromolar affinity to CB1, including one mixed partial agonist/antagonist viable for hit-to-lead phase. Finally, the computational procedure established in this work will be of use for future screening campaigns for novel CB1 ligands.

Identification of first-in-class plasmodium OTU inhibitors with potent anti-malarial activity

OTU proteases antagonize the cellular defense in the host cells and involve in pathogenesis. Intriguingly, P. falciparum, P. vivax, and P. yoelii have an uncharacterized and highly conserved viral OTU-like proteins. However, their structure, function or inhibitors have not been previously reported. To this end, we have performed structural modeling, small molecule screening, deconjugation assays to characterize and develop first-in-class inhibitors of P. falciparum, P. vivax, and P. yoelii OTU-like proteins. These Plasmodium OTU-like proteins have highly conserved residues in the catalytic and inhibition pockets similar to viral OTU proteins. Plasmodium OTU proteins demonstrated Ubiquitin and ISG15 deconjugation activities as evident by intracellular ubiquitinated protein content analyzed by western blot and flow cytometry. We screened a library of small molecules to determine plasmodium OTU inhibitors with potent anti-malarial activity. Enrichment and correlation studies identified structurally similar molecules. We have identified two small molecules that inhibit P. falciparum, P. vivax, and P. yoelii OTU proteins (IC50 values as low as 30 nM) with potent anti-malarial activity (IC50 of 4.1-6.5 µM). We also established enzyme kinetics, druglikeness, ADME, and QSAR model. MD simulations allowed us to resolve how inhibitors interacted with plasmodium OTU proteins. These findings suggest that targeting malarial OTU-like proteases is a plausible strategy to develop new anti-malarial therapies.

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.

ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

Summary

ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines.

Availability and implementation

http://chembioserver.vi-seem.eu.

Targeting homologous recombination (HR) repair mechanism for cancer treatment: discovery of new potential UCHL-3 inhibitors via virtual screening, molecular dynamics and binding mode analysis

UCHL3 (ubiquitin C-terminal hydrolase-L3) is a de-ubiquitinating enzyme involved in the homologous recombination repair mechanism of double-strand breaks (DBS) of the DNA. Multiple studies indicated that UCHL3 inhibitors could be used in combination therapy with high therapeutic efficacy against cancer thus highlighting the validity of directing research against UCHL3 as a druggable target in oncology. In this study, a combination of virtual screening methods was utilized to identify new potential UCHL3 inhibitors. A series of UCHL3 ligands were identified by applying a combination of cheminformatics and molecular modeling filtration techniques to a ChemBl database of over two million small molecules viz. Lipinski's Rule of Five, Veber's rule, pharmacophore model, Hierarchical molecular docking, Pan-assay Interference Compounds (PAINS) alerts, toxicity filter, and single-point Molecular mechanics Poisson/Boltzmann surface area (MM/PBSA) docking pose rescoring. This multi-layer filtration strategy led to the identification of twenty-one compounds as potential UCHL3 inhibitors that were subsequently subjected to a 50 ns molecular dynamics (MD) simulations predict the stability of their ligand-protein complexes. Furthermore, MM/PBSA calculations based on MD trajectories were performed, and the energy contribution per residue to the binding energy was calculated. Three compounds, 1, 2 and 3, were finally recognized as having the highest potential of being UCHL3 inhibitors. Therefore, those were used for binding mode analysis to the UCHL3 active site, leading to identification of four residues as key for binding viz. Pro8, Leu55, Val166, and Leu168.Communicated by Ramaswamy H. Sarma.

Drug-nutrient interactions: discovering prescription drug inhibitors of the thiamine transporter ThTR-2 (SLC19A3)

BACKGROUND

Transporter-mediated drug-nutrient interactions have the potential to cause serious adverse events. However, unlike drug-drug interactions, these drug-nutrient interactions receive little attention during drug development. The clinical importance of drug-nutrient interactions was highlighted when a phase III clinical trial was terminated due to severe adverse events resulting from potent inhibition of thiamine transporter 2 (ThTR-2; SLC19A3).

OBJECTIVE

In this study, we tested the hypothesis that therapeutic drugs inhibit the intestinal thiamine transporter ThTR-2, which may lead to thiamine deficiency.

METHODS

For this exploration, we took a multifaceted approach, starting with a high-throughput in vitro primary screen to identify inhibitors, building in silico models to characterize inhibitors, and leveraging real-world data from electronic health records to begin to understand the clinical relevance of these inhibitors.

RESULTS

Our high-throughput screen of 1360 compounds, including many clinically used drugs, identified 146 potential inhibitors at 200 μM. Inhibition kinetics were determined for 28 drugs with half-maximal inhibitory concentration (IC50) values ranging from 1.03 μM to >1 mM. Several oral drugs, including metformin, were predicted to have intestinal concentrations that may result in ThTR-2-mediated drug-nutrient interactions. Complementary analysis using electronic health records suggested that thiamine laboratory values are reduced in individuals receiving prescription drugs found to significantly inhibit ThTR-2, particularly in vulnerable populations (e.g., individuals with alcoholism).

CONCLUSIONS

Our comprehensive analysis of prescription drugs suggests that several marketed drugs inhibit ThTR-2, which may contribute to thiamine deficiency, especially in at-risk populations.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Curcumin-Synthetic Analogs Library Screening by Docking and Quantitative Structure-Activity Relationship Studies for AXL Tyrosine Kinase Inhibition in Cancers

AXL is an important drug target for cancers. Two-dimensional quantitative structure-activity relationship (2D-QSAR) tests were performed to elucidate a relationship between molecular structures and the activity of a series of 400 curcumin derivatives subjected to AXL kinase by ATP competition in the catalytic site. The partial least square regression method implanted in molecular operating environment software was applied to develop QSAR models, which were further validated for statistical significance by internal and external validation. The best model has proven to be statistically robust with a good predictive correlation of R ²  = 0.996 and a significant cross-validation correlation coefficient of q ²  = 0.707. Docking analysis reveled that three curcumin derivatives have the best affinity for AXL and formed a hydrogen bond with the important amino acid residues in the binding pocket. As treated in this article, the docking studies and 2D-QSAR approach will pave the way for the development of new drugs while highlighting curcumin and its derivatives.

Do Zebrafish Obey Lipinski Rules?

The use of zebrafish in whole organism phenotypic assays has become a valuable strategy throughout the drug discovery process. Zebrafish assays can be used not only to screen libraries of compounds at the earliest stages but also to evaluate advanced leads for their effects on specific biological pathways or for toxicity. However, when confronted with inactivity of a compound in a zebrafish assay, there are little data that can be used to judge if the compound is truly biologically inert or inactive due to a lack of permeability into the model organism. While medicinal chemistry principles suggest parameters that are predictive of human oral bioavailability, cellular permeability, and even bacterial permeability, there have been no such parameters developed for zebrafish absorption. To address this question, we compiled a set of 700 compounds reported in the literature to be active in zebrafish assays, evaluated their properties, and compared them to properties derived from a set of historical drugs and a set of recently approved oral drugs. While some properties overlap, the averages and 10th and 90th percentiles of molecular weight, octanol-water partition coefficient (logP), H-bond counts, and polar surface area for zebrafish-active compounds are statistically different from those of known drugs. This analysis should be useful to scientists interpreting structure-activity relationships based on data from zebrafish assays and help to inform the translation from fish to mammals.

Computer-based techniques for lead identification and optimization I: Basics

This chapter focuses on computational techniques for identifying and optimizing lead molecules, with a special emphasis on natural compounds. A number of case studies have been specifically discussed, such as the case of the naphthyridine scaffold, discovered through a structure-based virtual screening (SBVS) and proposed as the starting point for further lead optimization process, to enhance its telomeric RNA selectivity. Another example is the case of Liphagal, a tetracyclic meroterpenoid extracted fromAka coralliphaga, known as PI3Kα inhibitor, provide an evidence for the design of new active congeners against PI3Kα using molecular dynamics (MD) simulations. These are only two of the numerous examples of the computational techniques’ powerful in drug design and drug discovery fields. Finally, the design of drugs that can simultaneously interact with multiple targets as a promising approach for treating complicated diseases has been reported. An example of polypharmacological agents are the compounds extracted from mushrooms identified by means of molecular docking experiments. This chapter may be a useful manual of molecular modeling techniques used in the lead-optimization and lead identification processes.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Structure based virtual screening of novel noncompetitive antagonist of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor

The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) subtype ionotropic glutamate receptors are attractive antiepileptic targets responsible for mediating the majority of excitatory neurotransmission and plasticity. The noncompetitive antagonists obtain more and more attention as drug candidates for treatment of the neurological diseases involving excessive activity of AMPARs, due to they regulate AMPA receptors (AMPARs) activity independently of endogenous glutamate levels unlike the competitive antagonists. Development of novel AMPAR noncompetitive antagonists, which are safer and more efficacious than competitive antagonists, is highly under demand. Here, we present the discovery of novel antagonists against AMPAR through Structure-Based Virtual Screening (SBVS). Three compounds were successfully distinguished by several different filtering strategies, namely STOCK6S-10902, STOCK1N-49134 and STOCK5S-68665. The interaction mode of these compounds was further explored through molecular dynamics simulation, binding free energy calculation and the binding free energy decomposition. It is demonstrated that some residues within the binding pocket, which have been proved their importance in antagonist binding and gating, form strong hydrogen bond interactions with these three molecules. In particular, H-bond interactions with high occupancies between Ser516, Ser788 and STOCK6S-10902 and Ser516, Asn791 and STOCK1N-49134 were observed. The three hit compounds with new scaffolds and the detailed binding modes could potentially serve as a starting point for further optimization and development.

An Application of Computational Drug Repurposing Based on Transcriptomic Signatures

Drug repurposing is a methodology where already existing drugs are tested against diseases outside their initial usage, in order to reduce the high cost and long periods of new drug development. In silico drug repurposing further speeds up the process, by testing a large number of drugs against the biological signatures of known diseases. In this chapter, we present a step-by-step methodology of a transcriptomics-based computational drug repurposing pipeline providing a comprehensive guide to the whole procedure, from proper dataset selection to short list derivation of repurposed drugs which might act as inhibitors against the studied disease. The presented pipeline contains the selection and curation of proper transcriptomics datasets, statistical analysis of the datasets in order to extract the top over- and under-expressed gene identifiers, appropriate identifier conversion, drug repurposing analysis, repurposed drugs filtering, cross-tool screening, drug-list re-ranking, and results' validation.

Systematic polypharmacology and drug repurposing via an integrated L1000-based Connectivity Map database mining

Drug repurposing aims to find novel indications of clinically used or experimental drugs. Because drug data already exist, drug repurposing may save time and cost, and bypass safety concerns. Polypharmacology, one drug with multiple targets, serves as a basis for drug repurposing. Large-scale databases have been accumulated in recent years, and utilization and integration of these databases would be highly helpful for polypharmacology and drug repurposing. The Connectivity Map (CMap) is a database collecting gene-expression profiles of drug-treated human cancer cells, which has been widely used for investigation of polypharmacology and drug repurposing. In this study, we integrated the next-generation L1000-based CMap and an analytic Web tool, the L1000FWD, for systematic analyses of polypharmacology and drug repurposing. Two different types of anti-cancer drugs were used as proof-of-concept examples, including histone deacetylase (HDAC) inhibitors and topoisomerase inhibitors. We identified KM-00927 and BRD-K75081836 as novel HDAC inhibitors and mitomycin C as a topoisomerase IIB inhibitor. Our study provides a prime example of utilization and integration of the freely available public resources for systematic polypharmacology analysis and drug repurposing.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Drug repurposing in idiopathic pulmonary fibrosis filtered by a bioinformatics-derived composite score

Idiopathic Pulmonary Fibrosis (IPF) is a rare disease of the respiratory system in which the lungs stiffen and get scarred, resulting in breathing weakness and eventually leading to death. Drug repurposing is a process that provides evidence for existing drugs that may also be effective in different diseases. In this study, we present a computational pipeline having as input a number of gene expression datasets from early and advanced stages of IPF and as output lists of repurposed drugs ranked with a novel composite score. We have devised and used a scoring formula in order to rank the repurposed drugs, consolidating the standard repurposing score with structural, functional and side effects' scores for each drug per stage of IPF. The whole pipeline involves the selection of proper gene expression datasets, data preprocessing and statistical analysis, selection of the most important genes related to the disease, analysis of biological pathways, investigation of related molecular mechanisms, identification of fibrosis-related microRNAs, drug repurposing, structural and literature-based analysis of the repurposed drugs.

Platform for Unified Molecular Analysis: PUMA

We introduce a free platform for chemoinformatic-based diversity analysis and visualization of chemical space of user supplied data sets. Platform for Unified Molecular Analysis (PUMA) integrates metrics used to characterize compound databases including visualization of chemical space, scaffold content, and analysis of chemical diversity. The user's input is a file with SMILES, database names, and compound IDs. PUMA computes molecular properties of pharmaceutical relevance, Murcko scaffolds, and diversity analysis. The user can interactively navigate through the graphs and export image files and the raw data of the diversity calculations. The platform links two public online resources: Consensus Diversity Plots for the assessment of global diversity and Activity Landscape Plotter to analyze structure-activity relationships. Herein, we describe the functionalities of PUMA and exemplify its use through the analysis of compound databases of general interest. PUMA is freely accessible at the authors web-site https://www.difacquim.com/d-tools/ .

WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces

The concept of chemical space provides a convenient framework to analyze large collections of molecules by placing them in property spaces where distances represent similarities. Here we report webMolCS, a new type of web-based interface visualizing up to 5000 user-defined molecules in six different three-dimensional (3D) chemical spaces obtained by principal component analysis or similarity mapping of multidimensional property spaces describing composition (MQN: 42D molecular quantum numbers, SMIfp: 34D SMILES fingerprint), shapes and pharmacophores (APfp: 20D atom pair fingerprint, Xfp: 55D category extended atom pair fingerprint), and substructures (Sfp: 1024D binary substructure fingerprint, ECfp4:1024D extended connectivity fingerprint). Each molecule is shown as a sphere, and its structure appears on mouse over. The sphere is color-coded by similarity to the first compound in the list, by the list rank, or by a user-defined value, which reveals the relationship between any property encoded by these values and structural similarities. WebMolCS is freely available at www.gdb.unibe.ch .

Open chemoinformatic resources to explore the structure, properties and chemical space of molecules

Open chemoinformatic servers facilitate analysis of chemical space and structure–activity relationships.

Structure-activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r²=0.799 and a cross-validation of q²=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors.

Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data

Systemic approaches are essential in the discovery of disease-specific genes, offering a different perspective and new tools on the analysis of several types of molecular relationships, such as gene co-expression or protein-protein interactions. However, due to lack of experimental information, this analysis is not fully applicable. The aim of this study is to reveal the multi-potent contribution of statistical network inference methods in highlighting significant genes and interactions. We have investigated the ability of statistical co-expression networks to highlight and prioritize genes for breast cancer subtypes and stages in terms of: (i) classification efficiency, (ii) gene network pattern conservation, (iii) indication of involved molecular mechanisms and (iv) systems level momentum to drug repurposing pipelines. We have found that statistical network inference methods are advantageous in gene prioritization, are capable to contribute to meaningful network signature discovery, give insights regarding the disease-related mechanisms and boost drug discovery pipelines from a systems point of view.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Bioinformatics methods in drug repurposing for Alzheimer's disease

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.

Identification of small-molecule inhibitors of calcineurin-NFATc signaling that mimic the PxIxIT motif of calcineurin binding partners

Calcineurin (CN), a serine and threonine protein phosphatase that depends on Ca(2+) and calmodulin for its activity, is the target of the immunosuppressant drugs cyclosporin A (CsA) and tacrolimus (FK506). CN dephosphorylates and activates members of the NFATc (nuclear factor of activated T cells) family of transcription factors in T cells by binding to their conserved PxIxIT motif. Upon dephosphorylation, NFATc proteins translocate to the nucleus, where they stimulate the expression of genes encoding cytokines and chemokines that are required for T cell proliferation and the immune response. We performed a pharmacophore-based virtual screening of ~5.5 million commercially available, "drug-like" compounds to identify nonpeptidic compounds that inhibited the CN-dependent activation of NFATc signaling and that could serve as potential drug candidates for immunosuppressive therapy. Of 32 compounds that mimicked the PxIxIT motif, 7 competed with NFATc for binding to CN in vitro without interfering with the phosphatase activity of CN. Furthermore, in activated human CD4(+) T cells, four of the seven compounds inhibited the expression of NFATc-dependent genes, cytokine production, and cell proliferation, suggesting that these may have therapeutic potential as immunosuppressive agents.

Current insights into computer-aided immunotherapeutic design strategies

Drug designing costs as well as design of immunotherapeutic agents could be nearly halved through the involvement of computer-aided drug designing methods in discovery and research. The inter-disciplinary, time-, and money-consuming process of drug discovery is amended by the development of drug designing, the technique of creating or finding a molecule that can render stimulatory or inhibitory activity upon various biological organisms. Meanwhile, the advancements made within this scientific domain in the last couple of decades have significantly modified and affected the way new bioactive molecules have been produced by the pharmaceutical industry. In this regard, improvements made in hardware solutions and computational techniques along with their efficient integration with biological processes have revolutionized the in silico methods in speeding up the lead identification and optimization processes. In this review, we will discuss various methods of recent computer-aided drug designing techniques that forms the basis of modern day drug discovery projects.

Structure-based virtual screening for drug discovery: principles, applications and recent advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Anthropogenic reaction parameters – the missing link between chemical intuition and the available chemical space

Anthropogenic factors limit reaction parameters and thus the scope of synthetic chemistry, nevertheless, their role is both advantageous and critical.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.