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Key Points DNA-encoded chemical library technologies are increasingly being adopted in drug discovery for hit and lead generation. DNA-encoded chemistry enables the exploration of chemical spaces four to five orders of magnitude more deeply than is achievable by traditional high-throughput screening methods. DNA-encoded chemical library technology involves the creation of large mixtures of small molecules that are encoded with sequences or single-stranded or double-stranded DNA. High-affinity hits from such mixtures are identifiable by sequencing the DNA tags associated with each compound. DNA-encoded library technology began with a publication by Brenner and Lerner in 1992. The technology has subsequently evolved to be practised by several large pharmaceutical and biotechnology companies. DNA-directed synthesis is a related approach. In this method, the specificity of DNA base pairing serves for both encoding and synthesis. DNA-encoded library synthesis as it is currently practised is based on reactions that are tolerant to water. The creation of hundreds of millions of DNA-encoded library compounds is less expensive and more feasible than assembling a library of single compounds on milligram scale. DNA-encoded chemistry enables rapid and inexpensive syntheses and screening of vast chemical libraries, and is generating substantial interest and investment in the pharmaceutical industry. Here, Goodnow and colleagues provide an overview of the steps involved in the generation of DNA-encoded libraries, highlighting key applications and future directions for this technology. DNA-encoded chemical library technologies are increasingly being adopted in drug discovery for hit and lead generation. DNA-encoded chemistry enables the exploration of chemical spaces four to five orders of magnitude more deeply than is achievable by traditional high-throughput screening methods. Operation of this technology requires developing a range of capabilities including aqueous synthetic chemistry, building block acquisition, oligonucleotide conjugation, large-scale molecular biological transformations, selection methodologies, PCR, sequencing, sequence data analysis and the analysis of large chemistry spaces. This Review provides an overview of the development and applications of DNA-encoded chemistry, highlighting the challenges and future directions for the use of this technology.

Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery. Cellular senescence is involved in many disease processes but few senolytic compounds are currently known. Here, the authors report the discovery of three senolytics using machine learning models trained solely on published data, with large reductions in drug screening costs.

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy. This challenge is further compounded by reinfection after vaccination and the high likelihood of mutations, as well as the emergence of viral escape mutants that render SARS-CoV-2 spike glycoprotein-targeting vaccines ineffective. Employing de novo drug synthesis or repurposing to discover broad-spectrum antivirals that target highly conserved pathways within the viral machinery is a focus of current research. In a recent drug repurposing study, masitinib, a clinically safe drug against the human coronavirus OC43 (HCoV-OC43), was identified as an antiviral agent with effective inhibitory activity against the SARS-CoV-2 3CLpro. Masitinib is currently under clinical trial in combination with isoquercetin in hospitalized patients (NCT04622865). Nevertheless, masitinib has kinase-related side effects; hence, the development of masitinib analogs with lower anti–tyrosine kinase activity becomes necessary. In this study, in an attempt to address this limitation, we executed a comprehensive virtual workflow in silico to discover drug-like compounds matching selected pharmacophore features in the SARS-CoV-2 3CLpro-bound state of masitinib. We identified a novel lead compound, “masitinibL”, a drug-like analog of masitinib that demonstrated strong inhibitory properties against the SARS-CoV-2 3CLpro. In addition, masitinibL further displayed low selectivity for tyrosine kinases, which strongly suggests that masitinibL is a highly promising therapeutic that is preferable to masitinib.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

The challenge of assessing intracellular accumulation represents a major hurdle to the discovery of new antibiotics with Gram-negative activity. To address this, a high-throughput assay was developed to measure compound uptake and retention in Escherichia coli using LC/MS. 13,056 diverse small molecules were screened with two isogenic E. coli strains, a wild-type and a TolC-deleted mutant. Cell-associated concentrations of 8,410 compounds were determined and 6,416 compounds were classified either as retention-positive or -negative, with 45% (2,885) positives in the TolC mutant. Of these, 60% were not retained in the wild-type strain, indicating efficient efflux. No individual structural feature or physicochemical property explained the retention behavior. Machine learning (ML) models were trained using these results, and a gradient-boosted-tree model using 36 physicochemical compound descriptors proved most accurate. The ML model demonstrated robust performance across similar and dissimilar molecule subsets, showcasing its strong generalization and real-world predictive potential. An experimental validation of the tool was performed with a set of 540 new compounds and correctly predicted retention-positive cases in 77.8% and retention-negative in 74.4%. This assay and prediction tool could enhance Gram-negative antibiotic discovery, aiding in screening library design, computational structure-based drug design, and exploration of chemical space before synthesis.

Key Points A chemogenomic library is a collection of well-defined pharmacological agents. A hit from such a set in a phenotypic screen suggests that the annotated target or targets of the probe molecules are involved in the phenotypic perturbation. The creation and utility of a number of chemogenomic libraries have been described, by academia and industry, and some are commercially available. Chemogenomic screening has the potential to expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications include drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities. Target identification from phenotypic screening can benefit from the integration of small-molecule chemogenomics with genetic approaches, such as RNA-mediated interference and CRISPR–Cas9. Current limitations of chemogenomic screening include small-molecule polypharmacology, misannotation of biological activity and false-positive results (deriving from compound fluorescence or luciferase reporter binding) for example, although opportunities to overcome these issues, particularly through the incorporation of computational techniques, are emerging. 'Open innovation' and collaborative ventures across academia and industry are required to create and assemble the best pharmacological probes for chemogenomic libraries. Chemogenomic screening is increasingly being applied to expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Here, Jones and Bunnage discuss the principles of the creation and use of chemogenomic libraries, highlighting key examples and their applications, including target identification, drug repositioning and predictive toxicology. The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing — by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.The potential of therapeutically targeting RNA structures with small molecules is being increasingly recognized. Here, Disney and colleagues review strategies to identify, validate and optimize small-molecule RNA binders. Examples of existing RNA-targeted small molecules, as well as challenges and future directions in the field, are discussed.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments. Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

A high-throughput screen identifies inhibitors of the M. tuberculosis dormancy regulation system, DosRST, including compounds that inhibit autophosphorylation of the DosS and DosT sensor kinases and those that inhibit the catalytic heme of these kinases. The Mycobacterium tuberculosis (Mtb) DosRST two-component regulatory system promotes the survival of Mtb during non-replicating persistence (NRP). NRP bacteria help drive the long course of tuberculosis therapy; therefore, chemical inhibition of DosRST may inhibit the ability of Mtb to establish persistence and thus shorten treatment. Using a DosRST-dependent fluorescent Mtb reporter strain, a whole-cell phenotypic high-throughput screen of a ∼540,000 compound small-molecule library was conducted. The screen discovered novel inhibitors of the DosRST regulon, including three compounds that were subject to follow-up studies: artemisinin, HC102A and HC103A. Under hypoxia, all three compounds inhibit Mtb-persistence-associated physiological processes, including triacylglycerol synthesis, survival and antibiotic tolerance. Artemisinin functions by disabling the heme-based DosS and DosT sensor kinases by oxidizing ferrous heme and generating heme–artemisinin adducts. In contrast, HC103A inhibits DosS and DosT autophosphorylation activity without targeting the sensor kinase heme.