High-throughput and virtual screening: core lead discovery technologies move towards integration

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Valproate-coenzyme A conjugate blocks opening of receptor binding domains in the spike trimer of SARS-CoV-2 through an allosteric mechanism

The receptor-binding domains (RBDs) of the SARS-CoV-2 spike trimer exhibit "up" and "down" conformations often targeted by neutralizing antibodies. Only in the "up" configuration can RBDs bind to the ACE2 receptor of the host cell and initiate the process of viral multiplication. Here, we identify a lead compound (3-oxo-valproate-coenzyme A conjugate or Val-CoA) that stabilizes the spike trimer with RBDs in the down conformation. Val-CoA interacts with three R408 residues, one from each RBD, which significantly reduces the inter-subunit R408-R408 distance by ∼ 13 Å and closes the central pore formed by the three RBDs. Experimental evidence is presented that R408 is part of a triggering mechanism that controls the prefusion to postfusion state transition of the spike trimer. By stabilizing the RBDs in the down configuration, this and other related compounds can likely attenuate viral transmission. The reported findings for binding of Val-CoA to the spike trimer suggest a new approach for the design of allosteric antiviral drugs that do not have to compete for specific virus-receptor interactions but instead hinder the conformational motion of viral membrane proteins essential for interaction with the host cell. Here, we introduce an approach to target the spike protein by identifying lead compounds that stabilize the RBDs in the trimeric "down" configuration. When these compounds trimerize monomeric RBD immunogens as co-immunogens, they could also induce new types of non-ACE2 blocking antibodies that prevent local cell-to-cell transmission of the virus, providing a novel approach for inhibition of SARS-CoV-2.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Ubiquitination Regulators Discovered by Virtual Screening for the Treatment of Cancer

The ubiquitin-proteasome system oversees cellular protein degradation in order to regulate various critical processes, such as cell cycle control and DNA repair. Ubiquitination can serve as a marker for mutation, chemical damage, transcriptional or translational errors, and heat-induced denaturation. However, aberrant ubiquitination and degradation of tumor suppressor proteins may result in the growth and metastasis of cancer. Hence, targeting the ubiquitination cascade reaction has become a potential strategy for treating malignant diseases. Meanwhile, computer-aided methods have become widely accepted as fast and efficient techniques for early stage drug discovery. This review summarizes ubiquitination regulators that have been discovered via virtual screening and their applications for cancer treatment.

Natural bioactive compounds as a new source of promising G protein-coupled estrogen receptor (GPER) modulators: comprehensive in silico approach

Cancer ranks in second place among the cause of death worldwide. Cancer progress in multiple stages of carcinogenesis and metastasis programs through complex pathways. Sex hormones and their receptors are the major factors in promoting cancer progression. Among them, G protein-coupled estrogen receptor-1 (GPER) has shown to mediate cellular signaling pathways and cancer cell proliferation. However, the lack of GPER protein structure limited the search for new modulators. In this study, we curated an extensive database of natural products to discover new potential GPER modulators. We used a combination of virtual screening techniques to generate a homology model of GPER and subsequently used that for the screening of 30,926 natural products from a public database to identify potential active modulators of GPER. The best hits were further screened through the ADMET filter and confirmed by docking analysis. Moreover, molecular dynamics simulations of best hits were also carried out to assess the stability of the ligand-GPER complex. This study predicted several potential GPER modulators with novel scaffolds that could be further investigated and used as the core for the development of novel GPER modulators.Communicated by Ramaswamy H. Sarma.

Understanding the riboflavin biosynthesis pathway for the development of antimicrobial agents

Life on earth depends on the biosynthesis of riboflavin, which plays a vital role in biological electron transport processes. Higher mammals obtain riboflavin from dietary sources; however, various microorganisms, including Gram-negative pathogenic bacteria and yeast, lack an efficient riboflavin-uptake system and are dependent on endogenous riboflavin biosynthesis. Consequently, the inhibition of enzymes in the riboflavin biosynthesis pathway would allow selective toxicity to a pathogen and not the host. Thus, the riboflavin biosynthesis pathway is an attractive target for designing novel antimicrobial drugs, which are urgently needed to address the issue of multidrug resistance seen in various pathogens. The enzymes involved in riboflavin biosynthesis are lumazine synthase (LS) and riboflavin synthase (RS). Understanding the details of the mechanisms of the enzyme-catalyzed reactions and the structural changes that occur in the enzyme active sites during catalysis can facilitate the design and synthesis of suitable analogs that can specifically inhibit the relevant enzymes and stop the generation of riboflavin in pathogenic bacteria. The present review is the first compilation of the work that has been carried out over the last 25 years focusing on the design of inhibitors of the biosynthesis of riboflavin based on an understanding of the mechanisms of LS and RS. This review aimed to address the fundamental advances in our understanding of riboflavin biosynthesis as applied to the rational design of a novel class of inhibitors. These advances have been aided by X-ray structures of ligand-enzyme complexes, rotational-echo, double-resonance nuclear magnetic resonance spectroscopy, high-throughput screening, virtual screenings, and various mechanistic probes.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Structural basis for computational screening of non-steroidal androgen receptor ligands

IMPORTANCE OF THE FIELD

Deep structural and chemical understanding of the protein target and computational methods for detection of receptor-selective ligands are important for the early drug discovery in the steroid receptor field.

AREAS COVERED IN THIS REVIEW

This review focuses on the use of currently available structural information of the androgen receptor (AR) and known AR ligands to make computational strategies for the discovery of AR ligands in order to offer new chemical platforms for drug development.

WHAT THE READER WILL GAIN

AR is a challenging target for drug discovery and modeling even if there is a wealth of experimental data available. First, only the active structure of AR is currently known, which hampers the design of AR antagonists. Second, the structural similarity between the ligand-binding sites of AR and its mutated forms and closely related steroid receptors (SRs) such as progesterone receptors presents challenges for the development of drugs with receptor-selective function.

TAKE HOME MESSAGE

Research indicates that a very small chemical change in the structure of a non-steroidal ligand can cause a complete change in its activity. One source of this effect arises from binding to similar binding sites in related SRs and other proteins in the signaling pathway. Currently, computational methods are not able to predict the subtle differences between AR ligand activities but modeling does offer the possibility of generating new lead structures that might have the desired properties.

Comparison of three preprocessing filters efficiency in virtual screening: identification of new putative LXRbeta regulators as a test case

In silico screening methodologies are widely recognized as efficient approaches in early steps of drug discovery. However, in the virtual high-throughput screening (VHTS) context, where hit compounds are searched among millions of candidates, three-dimensional comparison techniques and knowledge discovery from databases should offer a better efficiency to finding novel drug leads than those of computationally expensive molecular dockings. Therefore, the present study aims at developing a filtering methodology to efficiently eliminate unsuitable compounds in VHTS process. Several filters are evaluated in this paper. The first two are structure-based and rely on either geometrical docking or pharmacophore depiction. The third filter is ligand-based and uses knowledge-based and fingerprint similarity techniques. These filtering methods were tested with the Liver X Receptor (LXR) as a target of therapeutic interest, as LXR is a key regulator in maintaining cholesterol homeostasis. The results show that the three considered filters are complementary so that their combination should generate consistent compound lists of potential hits.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time

Structure-based virtual screening today is basically organized as a sequential process where the molecules of a screening library are evaluated for instance with respect to their fit with a biological target. In this paper, we present a novel structure-based screening paradigm avoiding sequential searching and therefore enabling sublinear runtime behavior. We implemented the novel paradigm in the virtual screening tool TrixX and successfully applied it in screening experiments on four targets from relevant therapeutic areas. With the screening paradigm implemented in TrixX, we propose some important extensions and modifications to traditional virtual screening approaches: Instead of processing all compounds in the screening library sequentially, TrixX first analyzes the geometric and physicochemical binding site characteristics and then draws compounds with matching features from a compound catalog. The catalog organizes the compounds by their physicochemical and geometric features making use of relational database technology with indexed tables in order to support efficient queries for compounds with specific features. A key element of the compound catalog is a highly selective geometric descriptor that carries information on the type of functional groups of the compound, their Euclidian distance, the preferred interaction direction of each functional group, and the location of steric bulk around the triangle. In a re-docking experiment with 200 protein-ligand complexes, we could show that TrixX is able to correctly predict the location of ligand functional groups in co-crystallized complexes. In a retrospective virtual screening experiment for four different targets, the enrichment factors of TrixX are comparable to the enrichment factors of FlexX and FlexX-Scan. With computing times clearly below one second per compound, TrixX counts among the fastest virtual screening tools currently available and is nearly two orders of magnitude faster than standard FlexX.

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK (i) values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

Integrating research and development: the emergence of rational drug design in the pharmaceutical industry

Rational drug design is a method for developing new pharmaceuticals that typically involves the elucidation of fundamental physiological mechanisms. It thus combines the quest for a scientific understanding of natural phenomena with the design of useful technology and hence integrates epistemic and practical aims of research and development. Case studies of the rational design of the cardiovascular drugs propranolol, captopril and losartan provide insights into characteristics and conditions of this integration. Rational drug design became possible in the 1950s when theoretical knowledge of drug-target interaction and experimental drug testing could interlock in cycles of mutual advancement. The integration does not, however, diminish the importance of basic research for pharmaceutical development. Rather, it can be shown that still in the 1990s, linear processes of innovation and the close combination of practical and epistemic work were interdependent.

Novel technologies for virtual screening

There are several methods for virtual screening of databases of small organic compounds to find tight binders to a given protein target. Recent reviews in Drug Discovery Today have concentrated on screening by docking and by pharmacophore searching. Here, we complement these reviews by focusing on virtual screening methods that are based on analyzing ligand similarity on a structural level. Specifically, we concentrate on methods that exploit structural properties of the complete ligand molecules, as opposed to using just partial structural templates, such as pharmacophores. The in silico procedure of virtual screening (VS) and its relationship to the experimental procedure, HTS, is discussed, new developments in the field are summarized and perspectives on future research are offered.

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.