Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

The virtual screening (VS) is an important tool used in the modern drug discovery process to identify new leads and drug-like molecules for therapeutic interventions. With the rapid advancements in the computational hardware supported by advanced algorithmic progress and comprehensive data, the VS has gained fast momentum in drug discovery paradigm. The VS protocol is performed predominantly by using the ligand-based (LB) and structure-based (SB) VS methods with each one having its own merits and demerits. The LBVS method works on the similarity approach based on the physicochemical parameters, chemical functionality, and shape similarity of the ligands. The SBVS method works on the complementarity of the ligand with the target binding site and is more preferred than LBVS due to the consideration of both ligand and target information. Further in the SBVS, the available conformational information of the active chemical space helps in making reasonable decisions for the ligand selection. However, the limitations in scoring functions and incorrect pose prediction may result in the inaccurate SB models of limited performance in VS experiments. Various factors interplay in the development of successful VS models and the fine tuning of these factors leads to the efficient VS models with high sensitivity. In this chapter, some of the recent ligand- and structure-based approaches for virtual screening in drug discovery are described.

The holistic integration of virtual screening in drug discovery

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

Chapter 24 - Advances in structure-based virtual screening for drug discovery

The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

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.

Lymphatic filariasis and onchocerciasis caused by filarial nematodes are important diseases leading to considerable morbidity throughout tropical countries. Diethylcarbamazine (DEC), albendazole (ALB), and ivermectin (IVM) used in massive drug administration are not highly effective in killing the long-lived adult worms, and there is demand for the development of novel macrofilaricidal drugs affecting new molecular targets. A Ca2+ binding protein, calumenin, was identified as a novel and nematode-specific drug target for filariasis, due to its involvement in fertility and cuticle development in nematodes. As sterilizing and killing effects of the adult worms are considered to be ideal profiles of new drugs, calumenin could be an eligible drug target. Indeed, the Caenorhabditis elegans mutant model of calumenin exhibited enhanced drug acceptability to both microfilaricidal drugs (ALB and IVM) even at the adult stage, proving the roles of the nematode cuticle in efficient drug entry. Molecular modeling revealed that structural features of calumenin were only conserved among nematodes (C. elegans, Brugia malayi, and Onchocerca volvulus). Structural conservation and the specificity of nematode calumenins enabled the development of drugs with good target selectivity between parasites and human hosts. Structure-based virtual screening resulted in the discovery of itraconazole (ITC), an inhibitor of sterol biosynthesis, as a nematode calumenin-targeting ligand. The inhibitory potential of ITC was tested using a nematode mutant model of calumenin.

Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data, in this work, we apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multiple targets, the model trained by multi-task learning shows a better ability to adapt to a new target. Additional empirical study shows that other problems in drug discovery, such as the experimental drug-target affinity prediction, may also benefit from multi-task learning. Our results demonstrate that multi-task learning is a promising machine learning approach for docking-based virtual screening and accelerating the process of drug discovery.

This review provides a detailed analysis of the use of virtual library screening (VLS) in the drug discovery process. The first part is intended as a larger overview of the integrated VLS process. Small molecule and target macromolecule considerations will be described separately and will be subsequently integrated in a discussion of docking, scoring and evaluation. The second half of the review will focus on recent case studies that use VLS as part of an integrated drug discovery program. The case studies will illustrate the range of possible targets in VLS, provide an account of inclusive methodology and reveal the expectations for realistic goals. Recent efforts provide compelling evidence that VLS is successful when practiced in an integrated fashion involving synthetic, structural and computational expertise. Keywords: virtual screening, structure-based drug design, library design, drug discovery, diversity, filtering, ligand binding, docking

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.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC50 equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.

Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these, scaffold hopping is a widely used ligand-based lead identification strategy that facilitates clinical candidate discovery by seeking inhibitors with similar biological activity yet distinct scaffolds. In this study, we employed GeminiMol, a deep learning-based molecular representation framework that incorporates bioactive conformational space information. This approach enables ligand-based virtual screening by referencing known active compounds to identify potential hits with similar structural and bioactive conformational features. Using GeminiMol-based ligand screening method, we discovered a potent GluN1/GluN3A inhibitor, GM-10, from an 18-million-compound library. Notably, GM-10 features a completely different scaffold compared to known inhibitors. Subsequent validation using whole-cell patch-clamp recording confirmed its activity, with an IC50 of 0.98 ± 0.13 μM for GluN1/GluN3A. Further optimization is required to enhance its selectivity, as it exhibited IC50 values of 3.89 ± 0.79 μM for GluN1/GluN2A and 1.03 ± 0.21 μM for GluN1/GluN3B. This work highlights the potential of AI-driven molecular representation technologies to facilitate scaffold hopping and enhance similarity-based virtual screening for drug discovery.

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

Integrated in silico-in vitro strategy for screening of some traditional Egyptian plants for human aromatase inhibitors