Identification of novel antimalarial chemotypes via chemoinformatic compound selection methods for a high-throughput screening program against the novel malarial target, PfNDH2: increasing hit rate via virtual screening methods

Malaria is responsible for approximately 1 million deaths annually; thus, continued efforts to discover new antimalarials are required. A HTS screen was established to identify novel inhibitors of the parasite's mitochondrial enzyme NADH:quinone oxidoreductase (PfNDH2). On the basis of only one known inhibitor of this enzyme, the challenge was to discover novel inhibitors of PfNDH2 with diverse chemical scaffolds. To this end, using a range of ligand-based chemoinformatics methods, ~17000 compounds were selected from a commercial library of ~750000 compounds. Forty-eight compounds were identified with PfNDH2 enzyme inhibition IC(50) values ranging from 100 nM to 40 μM and also displayed exciting whole cell antimalarial activity. These novel inhibitors were identified through sampling 16% of the available chemical space, while only screening 2% of the library. This study confirms the added value of using multiple ligand-based chemoinformatic approaches and has successfully identified novel distinct chemotypes primed for development as new agents against malaria.     

Combination of ligand- and structure-based methods in virtual screening

1. Download : Download high-res image (115KB)
2. Download : Download full-size image

     

Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuraminidase

Avian influenza virus subtype H5N1 is a potential pandemic threat with human-adapted strains resistant to antiviral drugs. Although virtual screening (VS) against a crystal or relaxed receptor structure is an established method to identify potential inhibitors, the more dynamic changes within binding sites are neglected. To accommodate full receptor flexibility, we use AutoDock4 to screen the NCI diversity set against representative receptor ensembles extracted from explicitly solvated molecular dynamics simulations of the neuraminidase system. The top hits are redocked to the entire nonredundant receptor ensemble and rescored using the relaxed complex scheme (RCS). Of the 27 top hits reported, half ranked very poorly if only crystal structures are used. These compounds target the catalytic cavity as well as the newly identified 150- and 430-cavities, which exhibit dynamic properties in electrostatic surface and geometric shape. This ensemble-based VS and RCS approach may offer improvement over existing strategies for structure-based drug discovery.     

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.     

Docking and scoring in virtual screening for drug discovery: methods and applications

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.     

Novel technologies for virtual screening

There are several methods for virtual screening of databases of small organic compounds to find tight binders to a given protein target. Recent reviews in Drug Discovery Today have concentrated on screening by docking and by pharmacophore searching. Here, we complement these reviews by focusing on virtual screening methods that are based on analyzing ligand similarity on a structural level. Specifically, we concentrate on methods that exploit structural properties of the complete ligand molecules, as opposed to using just partial structural templates, such as pharmacophores. The in silico procedure of virtual screening (VS) and its relationship to the experimental procedure, HTS, is discussed, new developments in the field are summarized and perspectives on future research are offered.     

Exploiting PubChem for virtual screening

Importance of the field: PubChem is a public molecular information repository, a scientific showcase of the National Institutes of Health Roadmap Initiative. The PubChem database holds > 27 million records of unique chemical structures of compounds (compound ID) derived from nearly 70 million substance depositions (substance ID), and contains > 449,000 bioassay records with thousands of in vitro biochemical and cell-based screening bioassays established targeting > 7000 proteins and genes linking to > 1.8 million of substances.

Areas covered in this review: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling.

What the reader will gain: These publicly available data sets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the data sets, in particular the bioassay-associated false positives/negatives and highly imbalanced data sets in PubChem, also create major challenges. Several approaches regarding the modeling of PubChem data sets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed.

Take home message: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.     

Statistical tools for virtual screening

In large-scale virtual screening (VS) campaigns, data are often computed for millions of compounds to identify leads, but there remains the task of prioritizing VS "hits" for experimental assays and the dilemma of assessing true/false positives. We present two statistical methods for mining large databases: (1) a general scoring metric based on the VS signal-to-noise level within a compound neighborhood; (2) a neighborhood-based sampling strategy for reducing database size, in lieu of property-based filters.     

A novel approach for the virtual screening and rational design of anticancer compounds

A topological substructural approach to molecular design (TOSS-MODE) has been introduced for the selection and design of anticancer compounds. A quantitative model that discriminates anticancer compounds from the inactive ones in a training series was obtained. This model permits the correct classification of 91.43% of compounds in an external prediction set with only 1.43% of false actives and 7. 14% of false inactives. The model developed is then used in a simulation of a virtual search for Ras FTase inhibitors; 87% of the Ras FTase inhibitors used in this simulated search were correctly classified, thus indicating the ability of the TOSS-MODE model of finding lead compounds with novel structures and mechanism of action. Finally, a series of carbonucleosides was designed, and the compounds were classified as active/inactive anticancer compounds by using the model developed here. From the compounds so-designed, 20 were synthesized and evaluated experimentally for their antitumor effects on the proliferation of murine leukemia cells (L1210/0) and human T-lymphocyte cells (Molt4/C8 and CEM/0); 80% of these compounds were well-classified, as active or inactive, and only two pairs of isomeric compounds were false actives. The chloropurine derivatives were the most active compounds, especially compounds 6c, d.     

Receptor-ligand interaction-based virtual screening for novel Eg5/kinesin spindle protein inhibitors

Eg5/KSP is a promising mitotic spindle target for drug discovery in cancer chemotherapy and the development of agents against fungal diseases. A range of Eg5 targeting compounds identified by in vitro or cell-based screening is currently in development. We employed structure-based virtual screening of a database of 700,?000 compounds to identify three novel Eg5 inhibitors bearing quinazoline (24) or thioxoimidazolidine (30 and 37) scaffolds. The new compounds inhibit Eg5 ATPase activity, show growth inhibition in proliferation assays, and induce monoastral spindles in cells, the characteristic phenotype for Eg5 inhibiting agents. This is the first successful reported procedure for the identification of Eg5 inhibitors via receptor-ligand interaction-based virtual screening.     

Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors

A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease.     

An update of novel screening methods for GPCR in drug discovery

Introduction: G protein-coupled receptors (GPCRs) are the largest and most versatile group of cytomembrane receptors, comprising of approximately 300 non-sensory and druggable members. Traditional GPCR drug screening is based on radiometric competition binding assays, which are expensive and hazardous to human health. Furthermore, the paradox of high investment and low output, in terms of new drugs, highlights the need for more efficient and effective drug screening methods. Areas covered: This review summarizes non-radioactive assays assessing the ligand-receptor binding including: the fluorescence polarization assay, the TR-FRET assay and the surface plasmon resonance assay. It also looks at non-radioactive assays that assess receptor activation and signaling including: second messenger-based assays and β-arrestin recruitment-based assays. This review also looks at assays based on cellular phenotypic change. Expert opinion: GPCR signaling pathways look to be more complicated than previously thought. The existence of receptor allosteric sites and multireceptor downstream effectors restricts the traditional assay methods. The emergence of novel drug screening methods such as those for assessing β-arrestin recruitment and cellular phenotypic change may provide us with improved drug screening efficiency and effect.     

An update of novel screening methods for GPCR in drug discovery

Introduction: G protein-coupled receptors (GPCRs) are the largest and most versatile group of cytomembrane receptors, comprising of approximately 300 non-sensory and druggable members. Traditional GPCR drug screening is based on radiometric competition binding assays, which are expensive and hazardous to human health. Furthermore, the paradox of high investment and low output, in terms of new drugs, highlights the need for more efficient and effective drug screening methods.

Areas covered: This review summarizes non-radioactive assays assessing the ligand–receptor binding including: the fluorescence polarization assay, the TR-FRET assay and the surface plasmon resonance assay. It also looks at non-radioactive assays that assess receptor activation and signaling including: second messenger-based assays and β-arrestin recruitment-based assays. This review also looks at assays based on cellular phenotypic change.

Expert opinion: GPCR signaling pathways look to be more complicated than previously thought. The existence of receptor allosteric sites and multireceptor downstream effectors restricts the traditional assay methods. The emergence of novel drug screening methods such as those for assessing β-arrestin recruitment and cellular phenotypic change may provide us with improved drug screening efficiency and effect.     

Structure-based pharmacophore modeling and virtual screening to identify novel inhibitors for anthrax lethal factor

Inhibition of anthrax lethal factor (LF) has been reported to be a potent strategy for the treatment of anthrax; however, no effective LF inhibitors are currently available. In this study, a structure-based pharmacophore model was developed based on the co-crystallized structure of anthrax LF with the active inhibitor GM6001. The best pharmacophore model (denoted as SB_Hypo1), consisting of two hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic, was further validated using Gunner-Henry score method. The well-validated SB_Hypo1 was then used as a 3D-query in virtual screening to identify potential hits from NCI database. These hits were subsequently filtered by ADMET and validated by molecular docking experiments, and their binding stabilities were validated by 10-ns MD simulations. Finally, three hits were identified as potential leads based on their favorable binding interactions.     

New methodologies for ligand-based virtual screening

Computational screening of compound databases has become increasingly popular in pharmaceutical research. Virtual screening approaches can roughly be divided into target structure-based screening (often referred to as docking) and screening using active compounds as templates (ligand-based virtual screening). Ligand-based screening techniques essentially focus on comparative molecular similarity analysis of compounds with known and unknown activity, regardless of the methods or algorithms used. In this review, we first provide an overview of widely used ligand-based virtual screening approaches including various database filters and then discuss recent trends in this field and new methodological developments.     

Discovery of a novel histone deacetylase 8 inhibitor by virtual screening

In order to develop potent histone deacetylase inhibitors, a virtual screening approach was performed to discover novel lead structures. A commercial database containing about 167,000 molecules was in silico filtered by rule of five, zinc-binding groups, pharmacophore models, and binding pattern analysis. At last, three molecules were selected for enzyme inhibition assay, and one compound 02 has IC₅₀ of 1.6 μM against histone deacetylase 8 (HDAC8).     

Efficient molecular encoders for virtual screening

Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges. Combinational strategies were also used to improve the performance. Deep learning (DL)-based molecular encoders have attracted much attention for their automatic information extraction ability. In this review, we present an overview of two-dimensional-, three-dimensional-, and DL-based molecular encoders, summarize recent progress of VS using DL technologies, and propose a general framework of DL molecular encoder-based VS. Perspectives on the future directions of molecular representations and applications in the prediction of active compounds are also provided.