Integration of virtual and high-throughput screening.

Identifying the novel and potential starting lead compounds remains major challenge in drug discovery industry. In 90’s, high-throughput screening is a common practice in early stage of a project; screening of large number compounds to identify potential starting lead compounds which can modulate target of their interest. Although high-throughput screening has some remarkable successes in identifying potential drug leads, still HTVS is not fruitful as expected, due to the costly nature of chemicals, assays and also depends on the chemical space of the input library [1-4]. Over the years, tremendous research has carried out towards the designing of virtual libraries and virtual screening to identifying the potential starting leads, to reduce the cost of High throughput Screening and to improve the success rate. In current issue of “Current Topics in Medicinal Chemistry (CTMC)”, we focused on the recent trends in library design, Denovo studies, structure and ligand-based virtual screening approaches towards the medicinal chemistry and drug discovery topics and entitled the special topic with “Recent Trends in Library Design and Virtual Screening in Medicinal Chemistry and Drug Discovery”. Articles in this special issue have been contributed by experts from many parts of the world. In the first article, Drs. Rahul and Se Won Park discussed about discovery of a tuberculosis drug: bedaquiline, its anti-tuberculosis effects, mode of action and also discussed about computer-aided drug design approach to predict the binding mode for bedaquiline. In the next article, Drs. Prema Latha & Thomas discussed about successful application of structure-based drug design of Zanamivir (Relenza™) and oseltamivir (Tamiflu ® ), antiviral drugs. Also, discussed about application of computer-aided methods in identifying leads against influenza targets. Next article contributed by Drs. Harikishore & Yoon discusses about recent developments in denovo drug design and also explained successful application of the ligand and receptor based de novo drug design approaches. In the next article, Drs. Rodolpho & Carolina, summarizes the recent developments in virtual screening strategies and also highlights the recent achievements and as well as challenges. Also, discussed recent example of successful application for the identification of novel hit compounds for Trypanosoma cruzi sterol 14 α-demethylase (CYP51). In another article, Drs. Dimitar, Girinath, & Mati has covered a variety of studies have demonstrated the potential of machine- learning methods for predicting compounds as potential drug candidates. Also, provided an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. In next article, Drs. Evanthia & Zoe Cournia has covered an overview to the principles and applications of Structure-based Virtual Screening (VS) approaches and discussed on recent trends in library design, and as well as discuss limitations of the method.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Structure-based virtual screening is highly used in the early stages of drug discovery to identify new putative lead compounds for a given target. However, when a small molecule elicits a biological effect, but its target is unknown, or the side effects it causes arise from its undesired interaction with unknown counterparts, the identification of its interacting targets represents an indispensable task. The computational procedure named inverse virtual screening, which relies on docking a molecule (or a small set of compounds) against panels of target proteins to select the most promising complexes, could be useful to overcome these issues. Panels can contain thousands of proteins, and they must be correctly prepared to assure the best docking performance. Therefore, the preparation of panels of proteins collected in the Protein Data Bank ( www.rcsb.org ), if manually performed, may be costly in terms of time and efforts, and this can limit the applicability of this approach in high-throughput virtual screening workflows. We here show an automated workflow to speed up panel preparation and development, and to test its performance, this protocol was initially applied to a panel of 628 viral proteins and, afterward, to a panel of transferase proteins (2789 entries) to perform a large inverse virtual screening study, testing a small set of compounds synthesized in our laboratory. Tankyrase 2 (PARP 5b) was selected as their preferred target of interaction, and the predicted binding was validated by means of surface plasmon resonance experiments. This protocol is useful for the rapid identification of the interacting target for a bioactive compound; accordingly, it facilitates the re-evaluation of the pharmacological activity of known active compounds, addressing the repurposing and the polypharmacology concepts.

Chapter 7 - Virtual screening of phytochemicals for drug discovery

Cancer continues to remain a disease of scientific concern. Significant interest in targeting the Polo-Box-Domain (PBD) of Polo-like-kinase-1 (PLK-1) by novel ligands has arisen. The 'cleft' constituted by amino acid residues W414, H538, and K540 is the traditional target of PLK-1-PBD-inhibitors. However, this 'cleft' is merely a small part of the larger 'Y'-shaped cavity present therein. The objective of this study was to discover inhibitors of the PLK-1-PBD precisely directed against its trimodular 'Y'-pocket. High-throughput structure-based virtual screening (SBVS) of more than 5 million ligands against the aforementioned PLK-1 'Y'-pocket was performed. The SBVS hits were successively subjected to pass through various filters: VINA score ranking, toxicity checker, 'Special Criteria'-filtration, holistic tri-modular 'Y'-pocket interaction check, drug-likeness filters, and medicinal chemistry filters. Accordingly, we arrived at a single top ligand, 'SHAZ-i.' The top ligand, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide, displayed a robust interaction with the target crevice through 15 amino acid residues, an acceptable ΔG value of -7.8 kcal/mol, and a favorable pharmacokinetic profile with no adverse effects on humans. Hence, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide could emerge as a potent PLK-1-PBD inhibitor or might act as a 'seed' molecule for design of future inhibitors with a closely related backbone structure.

The Society of Biomolecular Imaging and Informatics First Annual Conference

The holistic integration of virtual screening in drug discovery

Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges. To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format. PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing largescale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research. PyComp effectively addresses some of the most pressing challenges in highthroughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds.

Computational virtual screening (VS) plays a vital role in early-stage drug discovery by enabling the efficient selection of candidate compounds and reducing associated costs. However, the absence of experimentally determined three-dimensional protein structures often limits the applicability of structure-based VS. Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. Yet, studies indicate that direct use of AlphaFold2-predicted structures often leads to suboptimal VS performance-likely because these structures fail to capture ligand-induced conformational changes (apo-to-holo transitions). To overcome this, we propose an approach that explores and modifies the structural space of AlphaFold2 predictions to generate conformations more amenable to VS. Our method deliberately alters the multiple sequence alignment (MSA) by introducing alanine mutations at key residues in the ligand-binding site, thereby inducing significant conformational shifts. The exploration process is guided by iterative ligand docking simulations, with mutation strategies optimized either by a genetic algorithm or via random search. Our evaluation shows that when sufficient active compounds are available, the genetic algorithm significantly enhances VS accuracy. In contrast, with limited active compound data, a random search strategy proves more effective. Moreover, our approach is particularly promising for targets that yield poor screening results when using experimentally determined structures from the PDB. Overall, these findings underscore the practical utility of modified AlphaFold2-derived structures in VS and expand the potential of computationally predicted protein models in drug discovery.

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.

Insect molting is regulated by 20-hydroxyecdsysone (20E), and the disruption of the molting process leads to death. To date, non-steroidal ecdysone agonists, diacylhydrazine (DAH)-type compounds, have been used as insecticides. We will review classical quantitative structure activity relationship (QSAR) studies for the in vivo and in vitro activity of DAHs and ecdysteroids. In the QSAR study for ecdysteroids, we applied three-dimensional QSAR, where hydrogen bond number was used as a parameter. The ligand-receptor complex was constructed by the homology modeling technique and the number of hydrogen bonds was counted. In recent drug design, computational approaches such as virtual (in silico) screening (VS) and high throughput screening (HTS) are often used. The VS procedure is classified as ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS); both are often used for the design of pharmaceuticals. We applied LBVS to find novel ecdysone agonists, in which ponasterone A (PoA), the most potent ecdysteroid, was used as a template molecule. Other computational techniques such as homology modeling and bioinformatics are also briefly reviewed.

Ebola Virus (EBOV) is an infectious disease that mainly affects the cardiovascular system. It belongs to the Filoviridae family, consisting of filamentous envelopes and non-segmented negative RNA genome. EBOV was initially identified in Sudan and Zaire (now named the Democratic Republic of Congo) around 1967. It is transmitted mainly by contact with secretions (blood, sweat, saliva, and tears) from infected wild animals, such as non-human primates and bats. It has gained more prominence in recent years due to the recent EBOV outbreaks that occurred from 2013 to 2016, resulting in approximately 28,000 infected individuals, with a mortality rate of 40- 70%, affecting mainly Liberia, Guinea, and Sierra Leone. Despite these alarming levels, there is still no FDA-approved drug for the effective treatment of these diseases. The most advanced drug to treat EBOV is remdesivir. However, it is a high-cost drug and is available only for intravenous use. In this sense, more investments are needed in the research focused on the development of new antiviral drugs. In this context, medicinal chemistry strategies have been improving and increasingly discovering new hits that can be used in the future as a treatment against these diseases. Thus, this review will address the main advances in medicinal chemistry, such as drug discovery through computational techniques (virtual screening and virtual high throughput screening), drug repurposing, phenotypic screening assays, and employing classical medicinal chemistry, such as bioisosterism, metabolism-based drug design, and the discovery of new inhibitors through natural products, thereby presenting several promising compounds that may contain the advance of these pathogens.

INTRODUCTION Since the late 1980s there have been striking advances, fueled by large increases in both industrial and NIH-funded academic research, that have revolutionized drug discovery. This period has seen the introduction of high-throughput screening (HTS), combinatorial chemistry, PC farms, Linux, SciFinder, structure-based design, virtual screening by docking, free-energy methods, absorption/distribution/metabolism/excretion (ADME) software, bioinformatics, routine biomolecular structure determination, structures for ion channels, G-protein-coupled receptors (GPCRs) and ribosomes, structure/activity relationships (SAR) obtained from nuclear magnetic resonance (SAR by NMR), fragment-based design, gene knockouts, proteomics, small interfering RNA (siRNA), and human genome sequences. The result is a much-accelerated progression from identification of biomolecular target to lead compound to clinical candidate. However, a serious concern is that the dramatic increase in drug discovery abilities and expenditures has not been paralleled by an increase in FDA approvals of new molecular entities. High demands for drug safety, broader and longer clinical trials, too much HTS, too little natural products research, and effective generic drugs for many once-pressing afflictions have all been suggested as contributors. Numerous corporate mergers and acquisitions may have also had adverse effects on productivity through distractions of reorganization and integration. Nevertheless, one should consider what the success would have been in the absence of the striking technical advances. Certainly, progress with some critical and challenging target classes such as kinases would have been greatly diminished, and the adverse impact on many cancer patients would have been profound.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

High throughput screening (HTS) is an important component of lead discovery, with virtual screening playing an increasingly important role. Both methods typically suffer from lack of sensitivity and specificity against their true biological targets. With ever-increasing screening libraries and virtual compound collections, it is now feasible to conduct follow-up experimental testing on only a small fraction of hits. In this context, advances in virtual screening that achieve enrichment of true actives among top-ranked compounds (“early recognition”) and, hence, reduce the number of hits to test, are highly desirable. The standard ligand-based virtual screening method for large compound libraries uses a molecular similarity search method that ranks the likelihood of a compound to be active against a drug target by its highest Tanimoto similarity to known active compounds. This approach assumes that the distributions of Tanimoto similarity values to all active compounds are identical (i.e., same mean and standard deviation)—an assumption shown to be invalid (Baldi and Nasr, 2010). Here, we introduce two methods that improve early recognition of actives by exploiting similarity information of all molecules. The first method ranks a compound by its highest z-score instead of its highest Tanimoto similarity, and the second by an aggregated score calculated from its Tanimoto similarity values to all known actives and inactives (or a large number of structurally diverse molecules when information on inactives is unavailable). Our evaluations, which use datasets of over 20 HTS campaigns downloaded from PubChem, indicate that compared to the conventional approach, both methods achieve a ~10% higher Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) score—a metric of early recognition. Given the increasing use of virtual screening in early lead discovery, these methods provide straightforward means to enhance early recognition.