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.     

Pharmacophore-based virtual screening: a review of recent applications

Importance of the field: In research relating to the development of new drugs, hit identification and validations are critical for successful optimization of candidates. To achieve rapid identification of new lead compounds, high-throughput screening assays have been employed in many pharmaceutical companies and laboratories. However, their success depends on the assay system relevant to in?vivo conditions and they are physically limited by the repertoire of compounds. As an alternative or complementary approach to high-throughput screening assays, virtual screening is an efficient method to identify drug candidates in silico from large chemical compound databases. Its usefulness has been verified by current applications that successfully retrieved hit and lead identifications against various disease targets. However, for better application, the scoring functions for distinguishing possible active and inactive compounds must beimproved. Areas covered in this review: In this review, we provide an overview of pharmacophore-based virtual screening methods with a special focus on their successful application towards finding hits against various diseasetargets. What the reader will gain: Readers will rapidly gain insight into the recent successful applications of pharmacophore-based virtual screening. They will acknowledge that this technique is a powerful and cost-effective alternative to high-throughput assays. Take home message: Although there are many hurdles yet to be resolved, virtual screening techniques will emerge as essential infrastructure and as a prerequisite for developing new lead compounds with therapeuticapplications.     

Pharmacophore-based virtual screening: a review of recent applications

The global pharmaceutical industry is described as facing an ‘innovation crisis’ following the ‘go-go-pharma’ era; in other words, the problem is one of ‘more money and fewer products’. Nevertheless, patients worldwide are awaiting innovative drugs. Therefore, the pharmaceutical industry has a duty to discover and develop novel drugs and medical technologies. Through universal coverage and reform of the patent system, the Japanese pharmaceutical industry has expanded greatly in line with the Japanese economy. However, in terms of scale and R&D investment, the Japanese pharmaceutical firms have lagged behind the foreign multinationals, which have undergone successive mergers and acquisitions. Meanwhile, it is true that several Japanese firms are playing an active role in overseas markets with their own blockbusters. This paper analyses and gives an overview of new trends in Japan’s pharmaceutical industry within the global context.     

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

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Modifications of the scoring function in FlexX for virtual screening applications

A modification of the hydrogen bond score in the docking program FlexX is presented. Hydrogen bonds formed in inaccessible regions of protein cavities thereby gain larger weight than others formed at the protein surface. The modified scoring function is tested with thrombin as a target. Secondly, a recently published knowledge-based scoring function is comparedto the FlexX scoring function in several database ranking experiments. This revised version was published online in June 2006 with corrections to the Cover Date.     

Optimization methods for virtual screening on novel computational architectures

The numerous virtual screening (VS) methods that are used today in drug discovery processes differ mainly by the way they model the receptor and/or ligand and by the approach to perform screening. All these methods have in common that they screen databases of chemical compounds containing up to millions of ligands i.e. ZINC database. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS method and the model. Fast docking methods with atomic resolution require a few minutes per ligand, while molecular dynamics-based approaches still require hundreds or thousands of hours per ligand. Therefore, the limitations of VS predictions are directly related to a lack of computational resources, a major bottleneck that prevents the application of detailed, high-accuracy models to VS. The current increase in available computer power at low cost due to novel computational architectures would enhance considerably the performance of the different VS methods and the quality and quantity of the conclusions we can get from screening. In this review, we will discuss recent trends in modeling techniques which, in combination with novel hardware platforms, yield order-of-magnitude improvements in the processing speeds of VS methods. We show the state of the art of VS methods as applied with novel computational architectures and the current trends of advanced computing.     

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.     

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.     

Pharmacophore-based virtual screening versus docking-based virtual screening： a benchmark comparison against eight targets

Aim:

This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

Methods:

All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors α (ERα), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

Results:

Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

Conclusion:

The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.     

Structure-based virtual screening: an overview

Enormous advances in genomics have resulted in a large increase in the number of potential therapeutic targets that are available for investigation. This growth in potential targets has increased the demand for reliable target validation, as well as technologies that can identify rapidly several quality lead candidates. Virtual screening, and in particular receptor-based virtual screening, has emerged as a reliable, inexpensive method for identifying leads. Although still an evolving method, advances in computational techniques have enabled virtual screening to have a positive impact on the discovery process. Here, the current strengths and weaknesses of the technology are discussed, and emphasis is placed on aspects of the work-flow of a virtual screening campaign, from preparation through to post-screening analysis.     

Pharmacophore-based virtual screening

Virtual screening (VS) is an important component of cheminformatics and molecular modeling. An abundance of structural information, indicated by both the ever-increasing availability of 3-dimensional (3D) protein structures and the readiness of free conformational databases of commercially available compounds, such as ZINC, supplies a broad platform for VS. At the same time, new technology enables the implementation of more accurate and sophisticated pharmacophore models and the screening of millions of compounds within a manageable period. Therefore, VS is expected to play a more important role in future drug discovery efforts. This paper will examine and compare the advantages and disadvantages of VS against experimental high-throughput screening (HTS). It will also evaluate pharmacophore-based VS against docking-based VS. The strategies leading to successful pharmacophore-based VS are outlined, including how to enumerate a conformational database efficiently, how to select chemical features for a specific pharmacophore model, how to incorporate excluded volumes to enhance the geographical restrictions, and how to optimize a pharmacophore model. Successful examples of pharmacophore-based VS will be presented.     

Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach

The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.     

Integration of virtual and high-throughput screening

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.     

A virtual screening approach to finding novel and potent antagonists at the melanin-concentrating hormone 1 receptor

Melanin-concentrating hormone (MCH) has been known to be an appetite-stimulating peptide for a number of years. However, it is only recently that MCH has been discovered to be the natural ligand for a previously "orphan" G-protein-coupled receptor, now designated MCH-1R. This receptor has been shown to mediate the effects of MCH on appetite and body weight, and consequently, drug discovery programs have begun to exploit this information in the search for MCH-1R antagonists for the treatment of obesity. In this paper, we report the rapid discovery of multiple, structurally distinct series of MCH-1R antagonists using a variety of virtual screening techniques. The most potent of these compounds (12) demonstrated an IC(50) value of 55 nM in the primary screen and exhibited antagonist properties in a functional cellular assay measuring Ca(2+) release. More potent compounds were identified by follow-up searches around the initial hit. A proposed binding mode for compound 12 in a homology model of the MCH-1R is also presented.     

Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor gamma activity: a combined virtual screening and biological assay approach

Most drugs currently employed in the treatment of type 2 diabetes either target the sulfonylurea receptor stimulating insulin release (sulfonylureas, glinides), or target the peroxisome proliferator-activated receptor (PPARgamma) improving insulin resistance (thiazolidinediones). Our work shows that sulfonylureas and glinides additionally bind to PPARgamma and exhibit PPARgamma agonistic activity. This activity was predicted in silico by virtual screening and confirmed in vitro in a binding assay, a transactivation assay, and by measuring the expression of PPARgamma target genes. Among the measured compounds, gliquidone and glipizide (two sulfonylureas), as well as nateglinide (a glinide), exhibit PPARgamma agonistic activity at concentrations comparable with those reached under pharmacological treatment. The most active of these compounds, gliquidone, is shown to be as potent as pioglitazone at inducing PPARgamma target gene expression. This dual mode of action of sulfonylureas and glinides may open new perspectives for the molecular pharmacology of antidiabetic drugs, because it provides evidence that drugs can be designed that target both the sulfonylurea receptor and PPARgamma. Targeting both receptors could increase pancreatic insulin secretion and improve insulin resistance. Glinides, sulfonylureas, and other acidified sulfonamides may be promising leads in the development of new PPARgamma agonists. In addition, we provide a unified concept of the PPARgamma binding ability of seemingly disparate compound classes.     

Physical and virtual screening methods for marine toxins and drug discovery targeting nicotinic acetylcholine receptors

Introduction: Nicotinic acetylcholine receptors (nAChRs) have been extensively studied because of their importance in physiological processes and their involvement in a number of muscle and neuronal human pathologies. However, the role of specific subtypes remains poorly understood due to the lack of selective nAChR probes. During the last decade, drug design strategies have been powered by a wide variety of natural compounds with diverse chemical structures, and by the structural characterization of several nAChRs structural homologs.

Areas covered: In this review, the authors present a short overview of nAChRs, and some natural sources of bioactive molecules targeting these receptors. The authors provide an emphasis on α-conotoxins from Conus venoms, which provide the most diverse selective antagonists of nAChRs known to date, as well briefly discussing macrocyclic imine toxins. The authors, furthermore, review valuable radioactive and non-radioactive methods used for discovering novel ligands targeting nAChRs and highlight high-throughput developments in receptor-binding and electrophysiological assays. Finally, the authors review the molecular modeling approaches used in the last few years with an aim to provide an overview of their potential to identify and optimize selective nAChR ligands.

Expert opinion: Recent years have provided new valuable techniques for the detection and identification of new nAChRs ligands, along with an increasing use of different molecular modeling tools. This furthering of knowledge has had an impact on the design and discovery of more potent and selective nAChRs ligands. There is still however a lack of high-resolution structural information that will require new developments.     

SPR-based fragment screening: advantages and applications

Fragment-based screening has recently evolved into a promising strategy in drug discovery, and a range of biophysical methods can be employed for fragment library screening. Relevant approaches, such as X-ray, NMR and tethering are briefly introduced focussing on their suitability for fragment-based drug discovery. In particular the application of surface plasmon resonance (SPR) techniques to the primary screening of large libraries comprising small molecules is discussed in detail. SPR is known to be a powerful tool for studying biomolecular interactions in a sensitive and label-free detection format. Advantages of SPR methods over more traditional assay formats are discussed and the application of available channel and array based SPR systems to biosensing are reviewed. Today, SPR protocols have been applied to secondary screening of compound libraries and hit conformation, but primary screening of large fragment libraries for drug discovery is often hampered by the throughput of available systems. Chemical microarrays, in combination with SPR imaging, can simultaneously generate affinity data for protein targets with up to 9,216 immobilized fragments per array. This approach has proven to be suitable for screening fragment libraries of up to 110,000 compounds in a high throughput fashion. The design of fragment libraries and appropriate immobilization chemistries are discussed, as well as suitable follow-up strategies for fragment hit optimization. Finally, described case studies demonstrate the successful identification of selective low molecular weight inhibitors for pharmacologically relevant drug targets through the SPR screening of fragment libraries.     

NMR fragment screening: Advantages and applications

Nuclear magnetic resonance (NMR)-fragment-screening is a useful tool for lead identification and optimization in cases where shallow binding pockets have to be targeted, where highly selective lead structures are to be designed, and where novel, best-fit scaffolds for combinatorial library design are required. Robust and complementary protein- and ligand-detected NMR-screening methods are available for target proteins of various molecular weights and from different expression systems. The advantages of the NMR assay over other analytical techniques are high sensitivity toward loose fragment binders, comparatively high throughput and the ability to offer unique structural binding site information.