Virtual screening: are we there yet?

The cost of pharmaceutical development has increased dramatically in recent years, and many assorted approaches have been developed to decrease both the time and costs associated with bringing a drug to the market. Among these methods is the use of in silico screening of compound databases for potential new lead compounds, commonly referred to as virtual screening (VS). Virtual screening has become an integral part of the early discovery process in pharmaceutical development, readily observed by the large number of methodologies that have been published to date. Other reviews have been published detailing the various types of virtual screening methods in use. This work will review some of the virtual screening approaches and strategies that have been attempted to identify compounds to launch medicinal chemistry campaigns. Understanding trends and drivers in VS should help to set expectations about how and when VS could be used and what it can and cannot deliver and how it can be integrated in a successful screening campaign and used in a complementary fashion to HTS.     

Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors

Glycogen synthase kinase-3beta (GSK-3beta) is a serine/threonine kinase that has recently emerged as a key target for neurodegenerative diseases and diabetes. As an initial step of our lead discovery program, we developed a virtual screen to discriminate known GSK-3beta inhibitors and inactive compounds using FlexX, FlexX-Pharm, and FlexE. The maximal enrichment factor (EF = 28) suggests that our protocol identifies potential GSK-3beta inhibitors effectively from large compound collections. The effectiveness of our screening protocol was further investigated by comparative experimental and virtual high-throughput screens (HTSs) performed for the same subset of our corporate library. Enrichment factors, the significantly higher hit rate of virtual screening (12.9%) than that of the HTS (0.55%), and also the comparison of active clusters suggest that our virtual screening protocol is an effective tool in GSK-3beta-based library focusing. Head-to-head comparison of true/false positives and negatives revealed the two approaches to be complementary rather than competitive.     

Virtual screening and its integration with modern drug design technologies

Drug discovery is a highly complex and costly process, which demands integrated efforts in several relevant aspects involving innovation, knowledge, information, technologies, expertise, R&D investments and management skills. The shift from traditional to genomics- and proteomics-based drug research has fundamentally transformed key R&D strategies in the pharmaceutical industry addressed to the design of new chemical entities as drug candidates against a variety of biological targets. Therefore, drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of small-molecules have attracted the attention of scientists from all over the world for the application of structure- and ligand-based drug design approaches. In this context, virtual screening technologies have largely enhanced the impact of computational methods applied to chemistry and biology and the goal of applying such methods is to reduce large compound databases and to select a limited number of promising candidates for drug design. This review provides a perspective of the utility of virtual screening in drug design and its integration with other important drug discovery technologies such as high-throughput screening (HTS) and QSAR, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.     

False positives in the early stages of drug discovery

High-throughput screening (HTS) is one of the most powerful approaches available for identifying new lead compounds for the growing catalogue of validated drug targets. However, just as virtual and experimental HTS have accelerated lead identification and changed drug discovery, they have also introduced a large number of peculiar molecules. Some of these have turned out to be interesting for further optimization, others to be dead ends when attempts are made to optimize their activity, typically after a great deal of time and resources have been devoted. Such false positive hits are still one of the key problems in the field of HTS and in the early stages of drug discovery in general. Many studies have been devoted to understanding the origins of false-positives, and the findings have been incorporated in filters and methods that can predict and eliminate problematic molecules from further consideration. This paper will focus on the structural classes and known mechanisms of nonleadlike false positives, together with experimental and computational methods for identifying such compounds.     

Strategies to generate biological reagents for kinase drug discovery

Introduction: High-throughput screening (HTS) has been and is likely to remain one of the most widely used tools for Hit identification in the pharmaceutical, biotechnology and academic sectors. It has evolved into a highly integrated and automated process enabling the screening of millions of compounds in a timely manner. It is of paramount importance that appropriate biological reagents are utilized in an HTS campaign as their quality and physiological relevance will influence the likelihood of the activities of any identified Hits translating in vivo. Areas covered: This article covers the strategies that can be used to efficiently design and generate biological reagents for the development of kinase assays and their subsequent use in HTS campaigns. The authors describe the variety of molecular biology and expression methodologies available to yield biological reagents of high quality, physiological relevance and amenable to kinase drug discovery. Expert opinion: The techniques now available for gene cloning and protein expression are vast and can be overwhelming. Therefore, we provide guidelines for the most effective route to generate high quality, physiologically relevant biological reagents for kinase drug discovery. The methods available for the generation of biological reagents have undergone significant advances and some of these have been driven by the requirements of HTS campaigns. If the approaches described herein are implemented, it is anticipated they will result in the generation of suitable biological reagents for the development of kinase assays for HTS campaigns.     

Advances in methods for therapeutic peptide discovery, design and development

Drug discovery and development are intense, lengthy and interdisciplinary processes. Traditionally, drugs were discovered by synthesizing compounds in time-consuming multi-step experimental investigations followed by in vitro and in vivo biological screening. Promising candidates were then further studied for their pharmacokinetic properties, metabolism and potential toxicity. Today, the process of drug discovery has been revolutionized due to the advances in genomics, proteomics, and bioinformatics. Efficient technologies such as combinatorial chemistry, high throughput screening (HTS), virtual screening, de novo design and structure-based drug design contribute greatly to drug discovery. Peptides are emerging as a novel class of drugs for cancer therapy, and many efforts have been made to develop peptide-based pharmacologically active compounds. This paper presents a review of current advances and novel approaches in experimental and computational drug discovery and design. We also present a novel bioactive peptide analogue, designed using the Resonant Recognition Model (RRM), and discuss its potential use for cancer therapeutics.     

Combining ligand-based and structure-based drug design in the virtual screening arena

The aim of virtual high-throughput screening is the identification of biologically relevant molecules among either tangible or virtual (large) collections of compounds. Likewise, high-throughput screening (HTS) and high-throughput virtual screening (HTVS) methods are becoming very important within the drug discovery process. HTVS methods can be categorised as either ‘ligand-based’ or ‘structure-based’ depending on if a direct knowledge of the three-dimensional target structure is required. A summary of the most promising computational approaches is reviewed. Advantages and shortcomings of the methodology are also discussed.     

Cheminformatics in anti-infective agents discovery

The existing chemical data such as those created by high throughput screening (HTS), structure-activity relationship (SAR) studies are converted into information as a result of storage and registration. Accessibility, manipulation, and data mining of such information make up the knowledge for drug development. Cheminformatics, exploiting the combination of chemical structural knowledge, biological screening, and data mining approaches is used to guide drug discovery and development and would assist by integrating complex series of rational selection of designed compounds with drug-like properties, building smarter focused libraries. This paper presents cheminformatics approaches and tools for designing and data mining of chemical databases and information. Many examples of success in lead identification and optimization in the area of anti-infective therapy have been discussed.     

Computational‐based discovery of FAK FERM domain chemical probes that inhibit HER2‐FAK cancer signaling

The N‐terminal FERM domain of focal adhesion kinase (FAK) contributes to FAK scaffolding and interacts with HER2, an oncogene and receptor tyrosine kinase. The interaction between HER2 and FAK drives resistance to FAK‐kinase domain inhibitors through FAK Y397 transphosphorylation and FAK re‐activation upon inhibition. As such, FAK FERM remains an attractive drug discovery target. In this report, we detail an alternative approach to targeting FAK through virtual screening‐based discovery of chemical probes that target FAK FERM. We validated the binding interface between HER2 and FAK using site‐directed mutagenesis and GST pull‐down experiments. We assessed the ligandability of key‐binding residues of HER2 and FAK utilizing computational tools. We developed a virtual screening method to screen ~200,000 compounds against the FAK FERM domain, identifying 20 virtual chemical probes. We performed GST pull‐down screening on these compounds, discovering two hits, VS4 and VS14, with nanomolar IC₅₀s in disrupting HER2‐FAK. We performed further testing, including molecular docking, immunofluorescence, phosphorylation, and cellular invasion assays to evaluate the compounds’ biological effects. One probe, VS14, was identified with the ability to block both auto‐ and transphosphorylation of Y397. In all, these studies identify two new probes that target FAK FERM, enabling future investigation of this domain.     

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION: Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways. AREAS COVERED: The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques. EXPERT OPINION: The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.     

Strategies and techniques for identification of novel bioactive compounds--CHI's second annual conference. 7-9 October 1998, Zurich, Switzerland

This conference on recent developments in the discovery of novel therapeutic candidates was organized by Amy Dasch (Cambridge Healthtech Institute, Newton Upper Falls, MA, USA; http://www.xensei.com/conferences). The conference provided an overview of all relevant aspects of the rapidly changing paradigms in drug research. Gene technology creates a vast number of new biological targets. The progress in combinatorial chemistry and high-throughput screening (HTS) is accompanied by the development of virtual libraries, large screening programs, and the generation of enormous sets of data. Correspondingly, the lectures covered such different topics as target identification and assay development, HTS technology, combinatorial library design and synthesis, chemoinformatics, and the integration of these components into the discovery of novel pharmaceutical compounds, the development of agricultural chemicals, and other applications. A most valuable addition was reports on case histories in drug development from pharmaceutical companies utilizing these technologies. About 100 scientists, many of them from European countries, attended the meeting. In total, 25 lectures were presented in four sessions: molecular diversity and library design; combinatorial synthesis; HTS; computational methodologies and chemoinformatics. Like other commercially organized conferences, this meeting was well-planned. The balance of speakers from small venture capital companies, large pharmaceutical and agricultural firms gave a broad overview of recent progress in the rational design, combinatorial synthesis, and HTS of new bioactive compounds, as well as on different approaches to handling large data sets and deriving structure-activity relationships from such data.     

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.     

On sampling of fragment space

Fragment-based lead discovery has over the years matured into an attractive alternative to high-throughput screening (HTS) for lead generation. Several techniques for screening libraries of typically 10(3)-10(4) fragments have been reported. In this work, the practical success rates that can be expected from the screening of fragment-like libraries was investigated via interrogating medicinal chemistry databases for several programs with virtual libraries created from commercially available reagents or with libraries of commercially available fragments. The results suggest that hits more potent than typically discovered in today's fragment-based screens can consistently be identified from realistically accessible compound sets under screening conditions similar to commonly used HTS protocols.     

Affinity-based screening techniques for enhancing lead discovery

Contemporary, rational small-molecule lead discovery methods, comprising target identification, assay development, high-throughput screening (HTS), hit characterization and medicinal chemistry optimization, dominate early-stage drug discovery strategies in many pharmaceutical companies. There is a growing disparity between the increasing cost of funding these methods and the decreasing number of new drugs reaching the market. New strategies must be adopted to reverse this trend. The use of genomics- and proteomics-based target discovery efforts can aid the process by dramatically increasing the number of novel, more highly validated targets entering the discovery process, but HTS must meet this increased demand with faster, cheaper technologies. Although activity-based screening strategies are typically efficient, allowing one scientist to interrogate tens of thousands of compounds per day, affinity-based screening strategies can allow much greater efficiency in the overall process. Affinity-based methods can play a role in both facilitating the screening of a greater number of targets and in efficiently characterizing the primary hits discovered.     

Artificial intelligence in virtual screening: Models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.     

High throughput pharmacology for drug discovery

High Throughput Screening (HTS) now plays an important role in the discovery of new lead compounds for novel therapeutic targets. The advantage of HTS over the conventional method, now termed as Low Throughput Screening (LTS), is that valuable compounds can be selected rapidly from a large number of samples with minimal human involvement. In spite of the growing awareness of HTS, the importance of the LTS in the drug discovery and development is still not changed. Advances in pharmacogenomics will also provide us many pharmacological targets, and thus increase the number of compounds that should be assayed by HTS and LTS. In this review, we will first describe the outline of HTS. We will next describe new approaches to develop and brush up the LTS: 1) screening method of drugs acting on ion channels by voltage-sensitive fluorescent dye, 2) functional assay method using reconstituted smooth muscle fiber, and 3) organ culture method as a useful model of vascular proliferative disease. These approaches, which work cooperatively with HTS, will contribute greatly to the development of new drugs.     

An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca's drug discovery programmes

Fragment-based lead generation (FBLG) has recently emerged as an alternative to traditional high throughput screening (HTS) to identify initial chemistry starting points for drug discovery programs. In comparison to HTS screening libraries, the screening sets for FBLG tend to contain orders of magnitude fewer compounds, and the compounds themselves are less structurally complex and have lower molecular weight. This report summarises the advent of FBLG within the industry and then describes the FBLG experience at AstraZeneca. We discuss (1) optimising the design of screening libraries, (2) hit detection methodologies, (3) evaluation of hit quality and use of ligand efficiency calculations, and (4) approaches to evolve fragment-based, low complexity hits towards drug-like leads. Furthermore, we exemplify our use of FBLG with case studies in the following drug discovery areas: antibacterial enzyme targets, GPCRs (melanocortin 4 receptor modulators), prostaglandin D2 synthase inhibitors, phosphatase inhibitors (protein tyrosine phosphotase 1B), and protease inhibitors (b-secretase).     

Chemoproteomics-driven drug discovery: addressing high attrition rates

The advent of multiple high-throughput technologies has brought drug discovery round almost full circle, from pharmacological testing of compounds in vivo to engineered molecular target assays and back to integrated phenotypic screens in cells and organisms. In the past, primary screens to identify new pharmacological agents involved administering compounds to an animal and monitoring a pharmacologic endpoint. For example, antihypertensive agents were identified by dosing spontaneously hypertensive rats with compounds and observing whether their blood pressure dropped. In taking this phenomenological approach, scientists were focused on the final goal, in this example lowering of blood pressure, rather than developing an understanding of the target, or targets, the compounds were impacting. With the evolution of rational target-based approaches, scientists were able to study the direct interaction of compounds with their intended targets, expecting that this would lead to more-selective and safer therapeutics. With the industrialization of screening, referred to as HTS, hundreds of thousands of compounds were screened in robot-driven assays against targets of interest (with this goal in mind). However, an unintentional outcome of the migration from in vivo primary screens to highly target-specific HTS assays was a reduction in biological context caused by the separation of the target from other cellular proteins and processes that might impact its function. Recognition of the potential consequences of this over-simplification drove the modification of HTS processes and equipment to be compatible with cellular assays.     

Pharmacognosy and reverse pharmacognosy: a new concept for accelerating natural drug discovery

Combinatorial chemistry and high-throughput screening (HTS) have led to the identification of numerous agents that are active and selective in vitro. Identifying drugs that are active in vivo, however, remains a challenge. Traditional medicinal cures based on natural materials have proven useful for many populations worldwide, representing huge and disperse tracts of knowledge that are sometimes neglected in Western research due to differences in the concepts of illness. In this review we introduce a new approach, termed 'reverse pharmacognosy' (from diverse molecules to plants), which can be coupled with pharmacognosy (from biodiverse plants to molecules). Reverse pharmacognosy utilizes new techniques, such as HTS, virtual screening and a knowledge database containing the traditional uses of plants. Integrating pharmacognosy and reverse pharmacognosy in the research process may provide an efficient and rapid tool for natural drug discovery.