Virtual Screening with AutoDock: Theory and Practice

IMPORTANCE TO THE FIELD: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. AREAS COVERED IN THIS REVIEW: We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. WHAT THE READER WILL GAIN: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. TAKE HOME MESSAGE: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.     

De novo design: balancing novelty and confined chemical space

Importance of the field: De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

Areas covered in this review: This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

What the reader will gain: The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

Take home message: De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.     

Current strategies for drug discovery through natural products

Importance to the field: Natural products are the most consistently successful source of drug leads, both historically and currently. Despite this, the use of natural products in industrial drug discovery has fallen out of favour. Natural products are likely to continue to be sources of new commercially viable drug leads because the chemical novelty associated with natural products is higher than that of any other source: this is particularly important when searching for lead molecules against newly discovered targets for which there are no known small molecule leads.

Areas to be covered: Current drug discovery strategies involving natural products are described in three sections: developments from traditionally used medicines, random testing of natural compounds on biological assays and use of virtual screening techniques with structures of natural products.

What the reader will gain: The reader will gain an insight into the potential for natural products in current drug discovery paradigms, particularly in the value of using natural products in virtual screening approaches.

Take home message: Drug discovery would be enriched if fuller use was made of the chemistry of natural products.     

Progress in structure-based drug design against influenza A virus

Introduction: The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment.

Areas covered: In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow.

Expert opinion: Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.     

Fragment-based lead discovery on G-protein-coupled receptors

Introduction: G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets.

Areas covered: This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress.

Expert opinion: With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project.     

The importance of employing computational resources for the automation of drug discovery

Introduction: The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action.

Areas covered: This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery.

Expert opinion: Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer–software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.     

Considering the economic principles of pharmaceutical R&D

Importance of the field: Virtual screening (VS) coupled with structural biology is a significantly important approach to increase the number and enhance the success of projects in lead identification stage of drug discovery process. Recent advances and future directions in estrogen therapy have resulted in great demand for identifying the potential estrogen receptor (ER) modulators with more activity and selectivity.

Areas covered in this review: This review presents the current state of the art in VS and structure–activity relationship of ER modulators in recent discovery, and discusses the strengths and weaknesses of the technology.

What the reader will gain: Readers will gain an overview of the current platforms of in silico screening for discovery of ER modulators; they will learn which structural information is significantly correlated with the bioactivity of ER modulators and what novel strategies should be considered for the creation of more effective chemical structures.

Take home message: With the goal of reducing toxicity and/or improving efficacy, challenges to the successful modeling of endocrine agents are proposed, providing new paradigms for the design of ER inhibitors.     

Advances with support vector machines for novel drug discovery

Introduction: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds.

Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges.

Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.     

Modern approaches to accelerate discovery of new antischistosomal drugs

ABSTRACT

Introduction: The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects.

Areas covered: Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors.

Expert opinion: Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.     

Getting the most out of PubChem for virtual screening

ABSTRACT

Introduction: With the emergence of the ‘big data’ era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.

Areas covered: This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.

Expert opinion: PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.     

In silico screening strategies for novel inhibitors of parasitic diseases

Introduction: Parasitic diseases are a major global problem causing long-term disability and death, with severe medical and psychological consequences around the world. Despite the prevalence of parasitic disease, the treatment options for many of these illnesses are still inadequate and there is a dire need for new antiparasitic drugs. In silico screening techniques, which are powerful strategies for hit generation, are widely being applied in the design of new ligands for parasitic diseases.

Areas covered: This article analyses the application of ligand- and structure-based virtual screening strategies against a variety of parasitic diseases and discusses the benefits of the integration between computational and experimental approaches toward the discovery of new antiparasitic agents. The analysis is illustrated by recent examples, with emphasis on the strategies reported within the past 2 years.

Expert opinion: Virtual screening techniques are powerful tools commonly used in drug discovery against parasitic diseases, which have provided new opportunities for the identification of several novel compound classes with antiparasitic activity.     

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.     

Advances in preclinical approaches to Chagas disease drug discovery

ABSTRACT

Introduction: Chagas disease affects 8–10 million people worldwide, mainly in Latin America. The current therapy for Chagas disease is limited to nifurtimox and benznidazole, which are effective in treating only the acute phase of the disease but with severe side effects. Therefore, there is an unmet need for new drugs and for the exploration of innovative approaches which may lead to the discovery of new effective and safe drugs for its treatment.

Areas covered: The authors report and discuss recent approaches including structure-based design that have led to the discovery of new promising small molecule candidates for Chagas disease which affect prime targets that intervene in the sterol pathway of T. cruzi. Other trypanosome targets, phenotypic screening, the use of artificial intelligence and the challenges with Chagas disease drug discovery are also discussed.

Expert opinion: The application of recent scientific innovations to the field of Chagas disease have led to the discovery of new promising drug candidates for Chagas disease. Phenotypic screening brought new hits and opportunities for drug discovery. Artificial intelligence also has the potential to accelerate drug discovery in Chagas disease and further research into this is warranted.     

QSAR of phytochemicals for the design of better drugs

Introduction: Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure–activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era.

Areas covered: QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area.

Expert opinion: This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.     

Non-stoichiometric inhibition in integrated lead finding – a literature review

ABSTRACT

Introduction: Non-stoichiometric inhibition summarizes different mechanisms by which low-molecular weight compounds can reproducibly inhibit high-throughput screening (HTS) and other lead finding assays without binding to a structurally defined site on their molecular target. This disqualifies such molecules from optimization by medicinal chemistry, and therefore their rapid elimination from screening hit lists is essential for productive and effective drug discovery.

Areas covered: This review covers recent literature that either investigates the various mechanisms behind non-stoichiometric inhibition or suggests assays and readouts to identify them. In addition, combination of the various methods to distill promising molecules out of raw primary hit lists step-by-step is considered. Emerging technologies to demonstrate target engagement in cells are also discussed.

Expert opinion: Over the last few years, awareness of non-stoichiometric inhibitors within screening libraries and HTS hit lists has considerably increased, not only in the pharmaceutical industry but also in the academic drug discovery community. This has resulted in a variety of methods to detect and handle such compounds. These range from in silico approaches to flag suspicious compounds, and counterassays to measure non-stoichiometric inhibition, to biophysical methods that positively demonstrate stoichiometric binding. In addition, novel technologies to verify target engagement within cells are becoming available. While still a time- and resource-consuming nuisance, non-stoichiometric inhibitors therefore do not fundamentally jeopardize the discovery of low molecular weight lead and drug candidates. Rather, they should be viewed as a manageable issue that with appropriate expertise can be overcome through integration of the above-mentioned approaches.     

How can high-throughput screening deliver drugs to treat atherosclerosis?

Importance of the field: Atherosclerosis is a progressive disease that is characterized by the accumulation of lipid-rich plaques within the artery walls. Despite the past 3 decades witnessing the most significant advances in the pharmacotherapy of atherosclerosis with statins, atherosclerosis is still one of the leading causes of mortality in industrialized and developing nations. The applications of high-throughput screening (HTS) have retrieved hits and lead compounds which may be further developed to new promising therapeutics to achieve more effective reductions in the risk of cardiovascular morbidity and mortality.

Areas covered in this review: The review provides a summary of potential drug targets other than HMG-CoA reductase (primary target of statins) and their application in biochemical or cell-based HTS assays used by pharmaceutical companies and academic laboratories for anti-atherosclerotic drug discovery.

What the reader will gain: The reader will gain an overview of the HTS strategies currently used in the development of anti-atherosclerotic agents. The reader is also provided with some abortive examples in anti-atherosclerotic drug discovery as well as the associated limitations and challenges of the process that HTS delivers new drugs to treat atherosclerosis.

Take home message: HTS can assist in the efficient discovery of new drugs towards the potential targets involved in the progress of atherosclerosis.     

Recent advances in pharmacophore modeling and its application to anti-influenza drug discovery

Introduction: The emergence of the highly pathogenic avian influenza (HPAI) H5N1 virus and the recent global circulation of H1N1 swine-origin influenza virus in 2009 have highlighted the need for new anti-influenza therapies. This has been made all the more important with the emergence of antiviral-resistant strains. Recent progress in achieving three-dimensional (3D) crystal structures of influenza viral proteins and efficient tools available for pharmacophore-based virtual screening are aiding us in the discovery and design of new antiviral compounds.

Areas covered: This review discusses pharmacophore modeling as a potential cost-effective and time-saving technology for new drug discovery as an alternative to high-throughput screening. Based on this technical platform, the authors discuss current progress and future prospects for developing novel influenza antivirals against pre-existing or emerging novel targets.

Expert opinion: Although it might be at an infant stage of development, the availability of the 3D crystal structures of influenza viral proteins is expected to accelerate the application of structure-based drug design (SBDD) and pharmacophore modeling. Furthermore, the neuraminidase inhibitor, one of the most successful examples of a SBDD, still receives great attention because of its superb antiviral activities and the resistance of influenza strains to oseltamivir. However, despite much success, pharmacophore-based virtual screening exhibits limited predictive power in hit identification. Further improvements in pharmacophore detection algorithms, proper combinations of in silico methods as well as judicious choosing of compounds are expected to improve the hit rate. With the help of these technologies, the discovery of anti-influenza agents will be accelerated.     

Using Drosophila models of neurodegenerative diseases for drug discovery

Introduction: Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and Huntington's disease are increasing in prevalence as our aging population increases in size. Despite this, currently there are no disease-modifying drugs available for the treatment of these conditions. Drosophila melanogaster is a highly tractable model organism that has been successfully used to emulate various aspects of these diseases in vivo. These Drosophila models have not been fully exploited in drug discovery and design strategies.

Areas covered: This review explores how Drosophila models can be used to facilitate drug discovery. Specifically, we review their uses as a physiologically-relevant medium to high-throughput screening tool for the identification of therapeutic compounds and discuss how they can aid drug discovery by highlighting disease mechanisms that may serve as druggable targets in the future. The reader will appreciate how the various attributes of Drosophila make it an unsurpassed model organism and how Drosophila models of neurodegeneration can contribute to drug discovery in a variety of ways.

Expert opinion: Drosophila models of human neurodegenerative diseases can make a significant contribution to the unmet need of disease-modifying therapeutic intervention for the treatment of these increasingly common neurodegenerative conditions.     

High-content screening for the discovery of pharmacological compounds: advantages,challenges and potential benefits of recent technological developments

Importance of the field: Screening compounds with cell-based assays and microscopy image-based analysis is an approach currently favored for drug discovery. Because of its high information yield, the strategy is called high-content screening (HCS).

Areas covered in this review: This review covers the application of HCS in drug discovery and also in basic research of potential new pathways that can be targeted for treatment of pathophysiological diseases. HCS faces several challenges, however, including the extraction of pertinent information from the massive amount of data generated from images. Several proposed approaches to HCS data acquisition and analysis are reviewed.

What the reader will gain: Different solutions from the fields of mathematics, bioinformatics and biotechnology are presented. Potential applications and limits of these recent technical developments are also discussed.

Take home message: HCS is a multidisciplinary and multistep approach for understanding the effects of compounds on biological processes at the cellular level. Reliable results depend on the quality of the overall process and require strong interdisciplinary collaborations.