The design and application of target-focused compound libraries

The evolution and application of RNA-focused small molecule libraries

RNA structure plays a role in nearly every disease. Therefore, approaches that identify tractable small molecule chemical matter that targets RNA and affects its function would transform drug discovery. Despite this potential, discovery of RNA-targeted small molecule chemical probes and medicines remains in its infancy. Advances in RNA-focused libraries are key to enable more successful primary screens and to define structure-activity relationships amongst hit molecules. In this review, we describe how RNA-focused small molecule libraries have been used and evolved over time and provide underlying principles for their application to develop bioactive small molecules. We also describe areas that need further investigation to advance the field, including generation of larger data sets to inform machine learning approaches.

Exploring chemical space for "druglike" small molecules in the age of AI

The announcement of 2024 Nobel Prize in Chemistry to Alphafold has reiterated the role of AI in biology and mainly in the domain of "drug discovery". Till few years ago, structure-based drug design (SBDD) has been the preferred experimental design in many academic and pharmaceutical R and D divisions for developing novel therapeutics. However, with the advent of AI, the drug design field especially has seen a paradigm shift in its R&D across platforms. If "drug design" is a game, there are two main players, the small molecule drug and its target biomolecule, and the rules governing the game are mainly based on the interactions between these two players. In this brief review, we will be discussing our efforts in improving the state-of-the-art technology with respect to small molecules as well as in understanding the rules of the game. The review is broadly divided into five sections with the first section introducing the field and the challenges faced and the role of AI in this domain. In the second section, we describe some of the existing small molecule libraries developed in our labs and follow-up this section with a more recent knowledge-based resource available for public use. In section four, we describe some of the screening tools developed in our laboratories and are available for public use. Finally, section five delves into how domain knowledge is improving the utilization of AI in drug design. We provide three case studies from our work to illustrate this work. Finally, we conclude with our thoughts on the future scope of AI in drug design.

Towards next-generation treatment options to combat Plasmodium falciparum malaria

Malaria, which is caused by infection of red blood cells with Plasmodium parasites, can be fatal in non-immune individuals if left untreated. The recent approval of the pre-erythrocytic vaccines RTS, S/AS01 and R21/Matrix-M has ushered in hope of substantial reductions in mortality rates, especially when combined with other existing interventions. However, the efficacy of these vaccines is partial, and chemotherapy remains central to malaria treatment and control. For many antimalarial drugs, clinical efficacy has been compromised by the emergence of drug-resistant Plasmodium falciparum strains. Therefore, there is an urgent need for new antimalarial medicines to complement the existing first-line artemisinin-based combination therapies. In this Review, we discuss various opportunities to expand the present malaria treatment space, appraise the current antimalarial drug development pipeline and highlight examples of promising targets. We also discuss other approaches to circumvent antimalarial resistance and how potency against drug-resistant parasites could be retained.

High-Throughput Screening of More Than 30,000 Compounds for Anthelmintics against Gastrointestinal Nematode Parasites

Gastrointestinal nematodes (GINs) are among the most common parasites of humans, livestock, and companion animals. GIN parasites infect 1-2 billion people worldwide, significantly impacting hundreds of millions of children, pregnant women, and adult workers, thereby perpetuating poverty. Two benzimidazoles with suboptimal efficacy are currently used to treat GINs in humans as part of mass drug administrations, with many instances of lower-than-expected or poor efficacy and possible resistance. Thus, new anthelmintics are urgently needed. However, screening methods for new anthelmintics using human GINs typically have low throughput. Here, using our novel screening pipeline that starts with human hookworms, we screened 30,238 unique small molecules from a wide range of compound libraries, including ones with generic diversity, repurposed drugs, natural derivatives, known mechanisms of action, as well as multiple target-focused libraries (e.g., targeting kinases, GPCRs, and neuronal proteins). We identified 55 compounds with broad-spectrum activity against adult stages of two evolutionary divergent GINs, hookworms (Ancylostoma ceylanicum) and whipworms (Trichuris muris). Based on known databases, the targets of these 55 compounds were predicted in nematode parasites. One novel scaffold from the diversity set library, F0317-0202, showed good activity (high motility inhibition) against both GINs. To better understand this novel scaffold's structure-activity relationships (SAR), we screened 28 analogs and created SAR models highlighting chemical and functional groups required for broad-spectrum activity. These studies validate our new and efficient screening pipeline at the level of tens of thousands of compounds and provide an important set of new GIN-active compounds for developing novel and broadly active anthelmintics.

High-throughput screening of more than 30,000 compounds for anthelmintics against gastrointestinal nematode parasites

Gastrointestinal nematodes (GINs) are amongst the most common parasites of humans, livestock, and companion animals. GIN parasites infect 1-2 billion people worldwide, significantly impacting hundreds of millions of children, pregnant women, and adult workers, thereby perpetuating poverty. Two benzimidazoles with suboptimal efficacy are currently used to treat GINs in humans as part of mass drug administrations, with many instances of lower-than-expected or poor efficacy and possible resistance. Thus, new anthelmintics are urgently needed. However, screening methods for new anthelmintics using human GINs typically have low throughput. Here, using our novel screening pipeline that starts with human hookworms, we screened 30,238 unique small molecules from a wide range of compound libraries, including ones with generic diversity, repurposed drugs, natural derivatives, known mechanisms of action, as well as multiple target-focused libraries (e.g., targeting kinases, GPCRs, and neuronal proteins). We identified 55 compounds with broad-spectrum activity against adult stages of two evolutionary divergent GINs, hookworms ( Ancylostoma ceylanicum ) and whipworms ( Trichuris muris ). Based on known databases, the targets of these 55 compounds were predicted in nematode parasites. One novel scaffold from the diversity set library, F0317-0202, showed good activity (high motility inhibition) against both GINs. To better understand this novel scaffold's structure-activity relationships (SAR), we screened 28 analogs and created SAR models highlighting chemical and functional groups required for broad-spectrum activity. These studies validate our new and efficient screening pipeline at the level of tens of thousands of compounds and provide an important set of new GIN-active compounds for developing novel and broadly-active anthelmintics.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing

Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.

Proposing lead compounds for the development of SARS-CoV-2 receptor-binding inhibitors

The COVID-19 pandemic has had deleterious effects on the world and demands urgent measures to find therapeutic agents to combat the current and related future outbreaks. The entry of SARS-CoV-2 into the host's cell is facilitated by the interaction between the viral spike receptor-binding domain (sRBD) and the human angiotensin-converting enzyme 2 (hACE2). Although the interface of sRBD involved in the sRBD-hACE2 interaction has been projected as a primary vaccine and drug target, currently no small-molecule drugs have been approved for covid-19 treatment targeting sRBD. Herein structure-based virtual screening and molecular dynamics (MD) simulation strategies were applied to identify novel potential small-molecule binders of the SARS-CoV-2 sRBD from an sRBD-targeted compound library as leads for the development of anti-COVID-19 drugs. The library was initially screened against sRBD by using the GOLD docking program whereby 19 compounds were shortlisted based on docking scores after using a control compound to set the selection cutoff. The stability of each compound in MD simulations was used as a further standard to select four hits namely T4S1820, T4589, E634-1449, and K784-7078. Analyses of simulations data showed that the four compounds remained stably bound to sRBD for ≥ 80 ns with reasonable affinities and interacted with pharmacologically important amino acid residues. The compounds exhibited fair solubility, lipophilicity, and toxicity-propensity characteristics that could be improved through lead optimization regimes. The overall results suggest that the scaffolds of T4S1820, E634-1449, and K784-7078 could serve as seeds for developing potent small-molecule inhibitors of SARS-CoV-2 receptor binding and cell entry.Communicated by Ramaswamy H. Sarma.

Allosteric drugs: New principles and design approaches

Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.

Screening Ultra-Large Encoded Compound Libraries Leads to Novel Protein-Ligand Interactions and High Selectivity

The DNA-encoded library (DEL) discovery platform has emerged as a powerful technology for hit identification in recent years. It has become one of the major parallel workstreams for small molecule drug discovery along with other strategies such as HTS and data mining. For many researchers working in the DEL field, it has become increasingly evident that many hits and leads discovered via DEL screening bind to target proteins with unique and unprecedented binding modes. This Perspective is our attempt to analyze reports of DEL screening with the purpose of providing a rigorous and useful account of the binding modes observed for DEL-derived ligands with a focus on binding mode novelty.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit Generation

Hit generation is a crucial step in drug discovery that will determine the speed and chance of success of identifying drug candidates. Many strategies are now available to identify chemical starting points, or hits, and each biological target warrants a tailored approach. In this set of best practices, we detail the essential approaches for target centric hit generation and the opportunities and challenges they come with. We then provide guidance on how to validate hits to ensure medicinal chemistry is only performed on compounds and scaffolds that engage the target of interest and have the desired mode of action. Finally, we discuss the design of integrated hit generation strategies that combine several approaches to maximize the chance of identifying high quality starting points to ensure a successful drug discovery campaign.

Structure, biochemistry, and gene expression patterns of the proline biosynthetic enzyme pyrroline-5-carboxylate reductase (PYCR), an emerging cancer therapy target

Proline metabolism features prominently in the unique metabolism of cancer cells. Proline biosynthetic genes are consistently upregulated in multiple cancers, while the proline catabolic enzyme proline dehydrogenase has dual, context-dependent pro-cancer and pro-apoptotic functions. Furthermore, the cycling of proline and Δ1-pyrroline-5-carboxylate through the proline cycle impacts cellular growth and death pathways by maintaining redox homeostasis between the cytosol and mitochondria. Here we focus on the last enzyme of proline biosynthesis, Δ1-pyrroline-5-carboxylate reductase, known as PYCR in humans. PYCR catalyzes the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate to proline and forms the reductive half of the proline metabolic cycle. We review the research on the three-dimensional structure, biochemistry, inhibition, and cancer biology of PYCR. To provide a global view of PYCR gene upregulation in cancer, we mined RNA transcript databases to analyze differential gene expression in 28 cancer types. This analysis revealed strong, widespread upregulation of PYCR genes, especially PYCR1. Altogether, the research over the past 20 years makes a compelling case for PYCR as a cancer therapy target. We conclude with a discussion of some of the major challenges for the field, including developing isoform-specific inhibitors, elucidating the function of the long C-terminus of PYCR1/2, and characterizing the interactome of PYCR.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Strategies to Combat Multi-Drug Resistance in Tuberculosis

"Drug resistance is an unavoidable consequence of the use of drugs; however, the emergence of multi-drug resistance can be managed by accurate diagnosis and tailor-made regimens."Antimicrobial resistance (AMR), is one of the most paramount health perils that has emerged in the 21st century. The global increase in drug-resistant strains of various bacterial pathogens prompted the World Health Organization (WHO) to develop a priority list of AMR pathogens. Mycobacterium tuberculosis (Mtb), an acid-fast bacillus that causes tuberculosis (TB), merits being one of the highest priority pathogens on this list since drug-resistant TB (DR-TB) accounts for ∼29% of deaths attributable to AMR. In recent years, funded collaborative efforts of researchers from academia, not-for-profit virtual R&D organizations and industry have resulted in the continuous growth of the TB drug discovery and development pipeline. This has so far led to the accelerated regulatory approval of bedaquiline and delamanid for the treatment of DR-TB. However, despite the availability of drug regimes, the current cure rate for multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens is 50% and 30%, respectively. It is to be noted that these regimens are administered over a long duration and have a serious side effect profile. Coupled with poor patient adherence, this has led to further acquisition of drug resistance and treatment failure. There is therefore an urgent need to develop new TB drugs with novel mechanism of actions (MoAs) and associated regimens.This Account recapitulates drug resistance in TB, existing challenges in addressing DR-TB, new drugs and regimens in development, and potential ways to treat DR-TB. We highlight our research aimed at identifying novel small molecule leads and associated targets against TB toward contributing to the global TB drug discovery and development pipeline. Our work mainly involves screening of various small molecule chemical libraries in phenotypic whole-cell based assays to identify hits for medicinal chemistry optimization, with attendant deconvolution of the MoA. We discuss the identification of small molecule chemotypes active against Mtb and subsequent structure-activity relationships (SAR) and MoA deconvolution studies. This is followed by a discussion on a chemical series identified by whole-cell cross-screening against Mtb, for which MoA deconvolution studies revealed a pathway that explained the lack of in vivo efficacy in a mouse model of TB and reiterated the importance of selecting an appropriate growth medium during phenotypic screening. We also discuss our efforts on drug repositioning toward addressing DR-TB. In the concluding section, we preview some promising future directions and the challenges inherent in advancing the drug pipeline to address DR-TB.

In crystallo screening for proline analog inhibitors of the proline cycle enzyme PYCR1

Pyrroline-5-carboxylate reductase 1 (PYCR1) catalyzes the biosynthetic half-reaction of the proline cycle by reducing Δ1-pyrroline-5-carboxylate (P5C) to proline through the oxidation of NAD(P)H. Many cancers alter their proline metabolism by up-regulating the proline cycle and proline biosynthesis, and knockdowns of PYCR1 lead to decreased cell proliferation. Thus, evidence is growing for PYCR1 as a potential cancer therapy target. Inhibitors of cancer targets are useful as chemical probes for studying cancer mechanisms and starting compounds for drug discovery; however, there is a notable lack of validated inhibitors for PYCR1. To fill this gap, we performed a small-scale focused screen of proline analogs using X-ray crystallography. Five inhibitors of human PYCR1 were discovered: l-tetrahydro-2-furoic acid, cyclopentanecarboxylate, l-thiazolidine-4-carboxylate, l-thiazolidine-2-carboxylate, and N-formyl l-proline (NFLP). The most potent inhibitor was NFLP, which had a competitive (with P5C) inhibition constant of 100 μm The structure of PYCR1 complexed with NFLP shows that inhibitor binding is accompanied by conformational changes in the active site, including the translation of an α-helix by 1 Å. These changes are unique to NFLP and enable additional hydrogen bonds with the enzyme. NFLP was also shown to phenocopy the PYCR1 knockdown in MCF10A H-RASV12 breast cancer cells by inhibiting de novo proline biosynthesis and impairing spheroidal growth. In summary, we generated the first validated chemical probe of PYCR1 and demonstrated proof-of-concept for screening proline analogs to discover inhibitors of the proline cycle.

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.

Chemoproteomic Profiling of a Pharmacophore-Focused Chemical Library

Pharmacophore-focused chemical libraries are continuously being created in drug discovery programs, yet screening assays to maximize the usage of such libraries are not fully explored. Here, we report a chemical proteomics approach to reutilizing a focused chemical library of 1,800 indole-containing molecules for discovering uncharacterized ligand-protein pairs. Gel-based protein profiling of the library using a photo-affinity indole probe 1 enabled us to find new ligands for glyoxalase 1 (Glo1), an enzyme involved in the detoxification of methylglyoxal. Structure optimization of the ligands yielded an inhibitor for Glo1 (9). Molecule 9 increased the cellular methylglyoxal levels in human cells and suppressed the osteoclast formation of mouse bone marrow-derived macrophages. X-ray structure analyses revealed that the molecule lies at a site abutting the substrate binding site, which is consistent with the enzyme kinetic profile of 9. Overall, this study exemplifies how chemical proteomics can be used to exploit existing focused chemical libraries.

A DNA-encoded library for the identification of natural product binders that modulate poly (ADP-ribose) polymerase 1, a validated anti-cancer target

Natural products have been an invaluable source of drug discovery, but their targets remain largely unknown. Natural products enriched DNA-encoded chemical libraries (nDELs) empower the researchers to rapidly and economically screen numerous natural products against various protein targets, and therefore promote the elucidation of the molecular mechanisms. In this work, we used poly (ADP-ribose) polymerase 1 (PARP1), as an example to explore the usage of nDEL for the functional natural products selection. We used late-stage modification approach to label three positive binders with unique DNA barcodes, whose dissociation constants range from sub-micromolar to micromolar. The selection criterion was set up according to the enrichment of these controls. Five natural products selected by this criterion directly bind to PARP1 in SPR, among which luteolin exhibits the highest inhibitory activity against PARP1. Moreover, luteolin selectively induces accumulation of DNA double-strand breaks and G2/M phase arrest in BRCA-deficient cells. All the findings from these investigations on luteolin support that PARP1 inhibition is one of the mechanisms for its anti-cancer activity.

In Silico Drug Repositioning for Chagas Disease

Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.

Discovery of trypanocidal coumarins with dual inhibition of both the glycerol kinase and alternative oxidase of Trypanosoma brucei brucei

African trypanosomiasis, sleeping sickness in humans or nagana in animals, is a potentially fatal neglected tropical disease and a threat to 65 million human lives and 100 million small and large livestock animals in sub-Saharan Africa. Available treatments for this devastating disease are few and have limited efficacy, prompting the search for new drug candidates. Simultaneous inhibition of the trypanosomal glycerol kinase (TGK) and trypanosomal alternative oxidase (TAO) is considered a validated strategy toward the development of new drugs. Our goal is to develop a TGK-specific inhibitor for coadministration with ascofuranone (AF), the most potent TAO inhibitor. Here, we report on the identification of novel compounds with inhibitory potency against TGK. Importantly, one of these compounds (compound 17) and its derivatives (17a and 17b) killed trypanosomes even in the absence of AF. Inhibition kinetics revealed that derivative 17b is a mixed-type and competitive inhibitor for TGK and TAO, respectively. Structural data revealed the molecular basis of this dual inhibitory action, which, in our opinion, will aid in the successful development of a promising drug to treat trypanosomiasis. Although the EC₅₀ of compound 17b against trypanosome cells was 1.77 µM, it had no effect on cultured human cells, even at 50 µM.-Balogun, E. O., Inaoka, D. K., Shiba, T., Tsuge, C., May, B., Sato, T., Kido, Y., Nara, T., Aoki, T., Honma, T., Tanaka, A., Inoue, M., Matsuoka, S., Michels, P. A. M., Watanabe, Y.-I., Moore, A. L., Harada, S., Kita, K. Discovery of trypanocidal coumarins with dual inhibition of both the glycerol kinase and alternative oxidase of Trypanosoma brucei brucei.

Insights into the development of chemical probes for RNA

Over the past decade, the RNA revolution has revealed thousands of non-coding RNAs that are essential for cellular regulation and are misregulated in disease. While the development of methods and tools to study these RNAs has been challenging, the power and promise of small molecule chemical probes is increasingly recognized. To harness existing knowledge, we compiled a list of 116 ligands with reported activity against RNA targets in biological systems (R-BIND). In this survey, we examine the RNA targets, design and discovery strategies, and chemical probe characterization techniques of these ligands. We discuss the applicability of current tools to identify and evaluate RNA-targeted chemical probes, suggest criteria to assess the quality of RNA chemical probes and targets, and propose areas where new tools are particularly needed. We anticipate that this knowledge will expedite the discovery of RNA-targeted ligands and the next phase of the RNA revolution.

Unpacking the Pathogen Box-An Open Source Tool for Fighting Neglected Tropical Disease

The Pathogen Box is a 400-strong collection of drug-like compounds, selected for their potential against several of the world's most important neglected tropical diseases, including trypanosomiasis, leishmaniasis, cryptosporidiosis, toxoplasmosis, filariasis, schistosomiasis, dengue virus and trichuriasis, in addition to malaria and tuberculosis. This library represents an ensemble of numerous successful drug discovery programmes from around the globe, aimed at providing a powerful resource to stimulate open source drug discovery for diseases threatening the most vulnerable communities in the world. This review seeks to provide an in-depth analysis of the literature pertaining to the compounds in the Pathogen Box, including structure-activity relationship highlights, mechanisms of action, related compounds with reported activity against different diseases, and, where appropriate, discussion on the known and putative targets of compounds, thereby providing context and increasing the accessibility of the Pathogen Box to the drug discovery community.

Application of Sequential Palladium Catalysis for the Discovery of Janus Kinase Inhibitors in the Benzo[ c]pyrrolo[2,3- h][1,6]naphthyridin-5-one (BPN) Series

The present account describes the discovery and development of a new benzo[ c]pyrrolo[2,3- h][1,6]naphthyridin-5-one (BPN) JAK inhibitory chemotype that has produced selective JAK inhibitors. Sequential palladium chemistry was optimized for the rapid access to a focused library of derivatives to explore the structure-activity relationships of the new scaffold. Several compounds from the series displayed potencies in the low nanomolar range against the four members of the JAK family with various selectivity profiles. Compound 20a, with an azetidine amide side chain, showed the best selectivity for JAK1 kinase vs JAK2, JAK3, and TYK2, with low nanomolar potency (IC₅₀ = 3.4 nM). On the other hand, BPNs 17b and 18 had good general activity against the JAK family with excellent kinome selectivity profiles. Many of the new BPNs inhibited JAK3-mediated STAT-5 phosphorylation, the production of inflammatory cytokines, and the proliferation of primary T cells. Moreover, BPN 17b showed very similar in vivo results to tofacitinib in a rheumatoid arthritis animal model.

Screening Library Design

Thanks to technological advances and a greater understanding of the biological and chemical natures of targets and related diseases, high-throughput screening (HTS) has been allowed to be faster, cheaper, and more accessible. Yet, despite these increased technologies and understandings, the frequency of novel and drugs are being approved each year has not being increasing over the years. 2017 was considered a "bumper" year with a total of 46 approved drugs, over double that of the previous year. However, it is thought that part of the problem that HTS has not lived up to expectations is because of the contents of current chemical libraries. Therefore, new methods to design screening libraries are of great interest.

Targeting RNA structure in SMN2 reverses spinal muscular atrophy molecular phenotypes

Modification of SMN2 exon 7 (E7) splicing is a validated therapeutic strategy against spinal muscular atrophy (SMA). However, a target-based approach to identify small-molecule E7 splicing modifiers has not been attempted, which could reveal novel therapies with improved mechanistic insight. Here, we chose as a target the stem-loop RNA structure TSL2, which overlaps with the 5' splicing site of E7. A small-molecule TSL2-binding compound, homocarbonyltopsentin (PK4C9), was identified that increases E7 splicing to therapeutic levels and rescues downstream molecular alterations in SMA cells. High-resolution NMR combined with molecular modelling revealed that PK4C9 binds to pentaloop conformations of TSL2 and promotes a shift to triloop conformations that display enhanced E7 splicing. Collectively, our study validates TSL2 as a target for small-molecule drug discovery in SMA, identifies a novel mechanism of action for an E7 splicing modifier, and sets a precedent for other splicing-mediated diseases where RNA structure could be similarly targeted.

Cheminformatics in Drug Discovery, an Industrial Perspective

Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions.

Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

Recent updates in the discovery and development of novel antimalarial drug candidates

Though morbidity and mortality due to malaria have declined in the last 15 years, emerging resistance to first-line artemisinin-based antimalarials, absence of efficacious vaccines and limited chemotherapeutic alternatives imperil the consolidation of these gains. As a blueprint to steer future designs of new medicines, malaria drug discovery recently adopted a descriptive proposal for the ideal candidate molecules and drugs likely to successfully progress into the final stages of clinical development. As an audit of recent developments in the chemotherapy of malaria in the last five years, this review captures a landscape of diverse molecules at various stages of drug development and discusses their progress. In brief, we also discuss how omics data on Plasmodium has been extensively leveraged to identify potential vaccine candidates and putative targets of molecules in development and clinical use as well as map loci implicit in their modes of resistance. Future perspective on malaria drug development should involve a reconciliation of some of the challenges of the target candidate profiles (TCPs), specifically TCP3, with the promise of effective anti-hypnozoite medicines. Similarly, with the recent development of a humanized mouse model that can evaluate the prophylactic potential of candidate drugs, we argue for increased effort at identifying more liver-stage molecules, which are often only secondarily prioritized in conventional screening programs.

Computer-Aided Drug Design: An Overview

The term drug design describes the search of novel compounds with biological activity, on a systematic basis. In its most common form, it involves modification of a known active scaffold or linking known active scaffolds, although de novo drug design (i.e., from scratch) is also possible. Though highly interrelated, identification of active scaffolds should be conceptually separated from drug design. Traditionally, the drug design process has focused on the molecular determinants of the interactions between the drug and its known or intended molecular target. Nevertheless, current drug design also takes into consideration other relevant processes than influence drug efficacy and safety (e.g., bioavailability, metabolic stability, interaction with antitargets).This chapter provides an overview on possible approaches to identify active scaffolds (including in silico approximations to approach that task) and computational methods to guide the subsequent optimization process. It also discusses in which situations each of the overviewed techniques is more appropriate.

Congestion game scheduling for virtual drug screening optimization

In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

Novel Antitubercular 6-Dialkylaminopyrimidine Carboxamides from Phenotypic Whole-Cell High Throughput Screening of a SoftFocus Library: Structure-Activity Relationship and Target Identification Studies

A BioFocus DPI SoftFocus library of ∼35 000 compounds was screened against Mycobacterium tuberculosis (Mtb) in order to identify novel hits with antitubercular activity. The hits were evaluated in biology triage assays to exclude compounds suggested to function via frequently encountered promiscuous mechanisms of action including inhibition of the QcrB subunit of the cytochrome bc₁ complex, disruption of cell–wall homeostasis, and DNA damage. Among the hits that passed this screening cascade, a 6-dialkylaminopyrimidine carboxamide series was prioritized for hit to lead optimization. Compounds from this series were active against clinical Mtb strains, while no cross-resistance to conventional antituberculosis drugs was observed. This suggested a novel mechanism of action, which was confirmed by chemoproteomic analysis leading to the identification of BCG_3193 and BCG_3827 as putative targets of the series with unknown function. Initial structure–activity relationship studies have resulted in compounds with moderate to potent antitubercular activity and improved physicochemical properties.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time

Exponential growth in the number of compounds with experimentally verified activity towards particular target has led to the emergence of various databases gathering data on biological activity. In this study, the ligands of family A of the G Protein-Coupled Receptors that are collected in the ChEMBL database were examined, and special attention was given to serotonin receptors. Sets of compounds were examined in terms of their appearance over time, they were mapped to the chemical space of drugs deposited in DrugBank, and the emergence of structurally new clusters of compounds was indicated. In addition, a tool for detailed analysis of the obtained visualizations was prepared and made available online at http://chem.gmum.net/vischem, which enables the investigation of chemical structures while referring to particular data points depicted in the figures and changes in compounds datasets over time.

Targeting of AMP-activated protein kinase: prospects for computer-aided drug design

INTRODUCTION

Dysregulation of energy homeostasis has been implicated in a number of human chronic diseases including diabetes, obesity, cancer, and inflammation. Given the functional attributes as a central regulator of energy homeostasis, AMP-activated protein kinase (AMPK) is emerging as a therapeutic target for these diseases, and lines of evidence have highlighted the need for rational and robust screening systems for identifying specific AMPK modulators with a therapeutic potential for preventing and/or curing these diseases. Areas covered: Here, the authors review the recent advances in the understanding of three-dimensional structures of AMPK in relationship with the regulatory mechanisms, potentials of AMPK as a therapeutic target in human chronic diseases, and prospects of computer-based drug design for AMPK. Expert opinion: Accumulating information of AMPK structure has provided us with deep insight into the molecular basis underlying the regulatory mechanisms, and further discloses several structural domains, which can be served for a target site for computer-based drug design. Molecular docking and simulations provides useful information about the binding sites between potent drugs and AMPK as well as a rational screening format to discover isoform-specific AMPK modulators. For these reasons, the authors suggest that computer-aided virtual screening methods hold promise as a rational approach for discovering more specific AMPK modulators.

A High-Throughput Screening-Compatible Strategy for the Identification of Inositol Pyrophosphate Kinase Inhibitors

Pharmacological tools-'chemical probes'-that intervene in cell signaling cascades are important for complementing genetically-based experimental approaches. Probe development frequently begins with a high-throughput screen (HTS) of a chemical library. Herein, we describe the design, validation, and implementation of the first HTS-compatible strategy against any inositol phosphate kinase. Our target enzyme, PPIP5K, synthesizes 'high-energy' inositol pyrophosphates (PP-InsPs), which regulate cell function at the interface between cellular energy metabolism and signal transduction. We optimized a time-resolved, fluorescence resonance energy transfer ADP-assay to record PPIP5K-catalyzed, ATP-driven phosphorylation of 5-InsP7 to 1,5-InsP8 in 384-well format (Z' = 0.82 ± 0.06). We screened a library of 4745 compounds, all anticipated to be membrane-permeant, which are known-or conjectured based on their structures-to target the nucleotide binding site of protein kinases. At a screening concentration of 13 μM, fifteen compounds inhibited PPIP5K >50%. The potency of nine of these hits was confirmed by dose-response analyses. Three of these molecules were selected from different structural clusters for analysis of binding to PPIP5K, using isothermal calorimetry. Acceptable thermograms were obtained for two compounds, UNC10112646 (Kd = 7.30 ± 0.03 μM) and UNC10225498 (Kd = 1.37 ± 0.03 μM). These Kd values lie within the 1-10 μM range generally recognized as suitable for further probe development. In silico docking data rationalizes the difference in affinities. HPLC analysis confirmed that UNC10225498 and UNC10112646 directly inhibit PPIP5K-catalyzed phosphorylation of 5-InsP7 to 1,5-InsP8; kinetic experiments showed inhibition to be competitive with ATP. No other biological activity has previously been ascribed to either UNC10225498 or UNC10112646; moreover, at 10 μM, neither compound inhibits IP6K2, a structurally-unrelated PP-InsP kinase. Our screening strategy may be generally applicable to inhibitor discovery campaigns for other inositol phosphate kinases.

A graph-based approach to construct target-focused libraries for virtual screening

Background

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family.

Methods

Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns.

Results

We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated.

Conclusions

eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstract