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To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch . Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

Aberrant activation of innate immune pathways is associated with a variety of diseases. Progress in understanding the molecular mechanisms of innate immune pathways has led to the promise of targeted therapeutic approaches, but the development of drugs that act specifically on molecules of interest remains challenging. Here we report the discovery and characterization of highly potent and selective small-molecule antagonists of the stimulator of interferon genes (STING) protein, which is a central signalling component of the intracellular DNA sensing pathway 1 , 2 . Mechanistically, the identified compounds covalently target the predicted transmembrane cysteine residue 91 and thereby block the activation-induced palmitoylation of STING. Using these inhibitors, we show that the palmitoylation of STING is essential for its assembly into multimeric complexes at the Golgi apparatus and, in turn, for the recruitment of downstream signalling factors. The identified compounds and their derivatives reduce STING-mediated inflammatory cytokine production in both human and mouse cells. Furthermore, we show that these small-molecule antagonists attenuate pathological features of autoinflammatory disease in mice. In summary, our work uncovers a mechanism by which STING can be inhibited pharmacologically and demonstrates the potential of therapies that target STING for the treatment of autoinflammatory disease. The discovery and characterization of small-molecule antagonists that inhibit the stimulator of interferon genes (STING) protein may help to develop therapies for the treatment of autoinflammatory disease.

Photoactivatable derivatives of the bright and photostable Janelia Fluor dyes enable improved multicolor single-particle tracking and facile localization microscopy in cells. Small-molecule fluorophores are important tools for advanced imaging experiments. We previously reported a general method to improve small, cell-permeable fluorophores which resulted in the azetidine-containing 'Janelia Fluor' (JF) dyes. Here, we refine and extend the utility of these dyes by synthesizing photoactivatable derivatives that are compatible with live-cell labeling strategies. Once activated, these derived compounds retain the superior brightness and photostability of the JF dyes, enabling improved single-particle tracking and facile localization microscopy experiments.

Investigations into the biosynthetic pathways of three families of actin-targeting macrolides lead to insights into their convergent or combinatorial evolution, along with the identification of the first free-living bacterial source of macroalga-derived luminaolides. Actin-targeting macrolides comprise a large, structurally diverse group of cytotoxins isolated from remarkably dissimilar micro- and macroorganisms. In spite of their disparate origins and structures, many of these compounds bind actin at the same site and exhibit structural relationships reminiscent of modular, combinatorial drug libraries. Here we investigate biosynthesis and evolution of three compound groups: misakinolides, scytophycin-type compounds and luminaolides. For misakinolides from the sponge Theonella swinhoei WA, our data suggest production by an uncultivated 'Entotheonella' symbiont, further supporting the relevance of these bacteria as sources of bioactive polyketides and peptides in sponges. Insights into misakinolide biosynthesis permitted targeted genome mining for other members, providing a cyanobacterial luminaolide producer as the first cultivated source for this dimeric compound family. The data indicate that this polyketide family is bacteria-derived and that the unusual macrolide diversity is the result of combinatorial pathway modularity for some compounds and of convergent evolution for others.

Molecular glue compounds induce protein–protein interactions that, in the context of a ubiquitin ligase, lead to protein degradation 1 . Unlike traditional enzyme inhibitors, these molecular glue degraders act substoichiometrically to catalyse the rapid depletion of previously inaccessible targets 2 . They are clinically effective and highly sought-after, but have thus far only been discovered serendipitously. Here, through systematically mining databases for correlations between the cytotoxicity of 4,518 clinical and preclinical small molecules and the expression levels of E3 ligase components across hundreds of human cancer cell lines 3 – 5 , we identify CR8—a cyclin-dependent kinase (CDK) inhibitor 6 —as a compound that acts as a molecular glue degrader. The CDK-bound form of CR8 has a solvent-exposed pyridyl moiety that induces the formation of a complex between CDK12–cyclin K and the CUL4 adaptor protein DDB1, bypassing the requirement for a substrate receptor and presenting cyclin K for ubiquitination and degradation. Our studies demonstrate that chemical alteration of surface-exposed moieties can confer gain-of-function glue properties to an inhibitor, and we propose this as a broader strategy through which target-binding molecules could be converted into molecular glues. The cyclin-dependent kinase inhibitor CR8 acts as a molecular glue compound by inducing the formation of a complex between CDK12–cyclin K and DDB1, which results in the ubiquitination and degradation of cyclin K.

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.

Notch pathway signaling is implicated in several human cancers. Aberrant activation and mutations of Notch signaling components are linked to tumor initiation, maintenance, and resistance to cancer therapy. Several strategies, such as monoclonal antibodies against Notch ligands and receptors, as well as small-molecule γ-secretase inhibitors (GSIs), have been developed to interfere with Notch receptor activation at proximal points in the pathway. However, the use of drug-like small molecules to target the downstream mediators of Notch signaling, the Notch transcription activation complex, remains largely unexplored. Here, we report the discovery of an orally active small-molecule inhibitor (termed CB-103) of the Notch transcription activation complex. We show that CB-103 inhibits Notch signaling in primary human T cell acute lymphoblastic leukemia and other Notch-dependent human tumor cell lines, and concomitantly induces cell cycle arrest and apoptosis, thereby impairing proliferation, including in GSI-resistant human tumor cell lines with chromosomal translocations and rearrangements in Notch genes. CB-103 produces Notch loss-of-function phenotypes in flies and mice and inhibits the growth of human breast cancer and leukemia xenografts, notably without causing the dose-limiting intestinal toxicity associated with other Notch inhibitors. Thus, we describe a pharmacological strategy that interferes with Notch signaling by disrupting the Notch transcription complex and shows therapeutic potential for treating Notch-driven cancers.

Since the discovery of AMP-activated protein kinase (AMPK) as a central regulator of energy homeostasis, many exciting insights into its structure, regulation and physiological roles have been revealed. While exercise, caloric restriction, metformin and many natural products increase AMPK activity and exert a multitude of health benefits, developing direct activators of AMPK to elicit beneficial effects has been challenging. However, in recent years, direct AMPK activators have been identified and tested in preclinical models, and a small number have entered clinical trials. Despite these advances, which disease(s) represent the best indications for therapeutic AMPK activation and the long-term safety of such approaches remain to be established.AMP-activated protein kinase (AMPK) is a central regulator of energy homeostasis that is activated by physiological regulators associated with health and longevity. Here, Steinberg and Carling provide an overview of the physiological functions of AMPK and discuss the potential of this enzyme as a therapeutic target across diverse disease areas. Pharmacological activation of AMPK and the associated drug development challenges are assessed.

High-throughput screening identifies an inhibitor of the interaction between α- and β-subunits of the Mycobacterium tuberculosis (Mtb) tryptophan synthase, TrpAB, that allows for defining TrpAB as essential for Mtb infection, independent of a T cell response. New antibiotics with novel targets are greatly needed. Bacteria have numerous essential functions, but only a small fraction of such processes—primarily those involved in macromolecular synthesis—are inhibited by current drugs. Targeting metabolic enzymes has been the focus of recent interest, but effective inhibitors have been difficult to identify. We describe a synthetic azetidine derivative, BRD4592, that kills Mycobacterium tuberculosis (Mtb) through allosteric inhibition of tryptophan synthase (TrpAB), a previously untargeted, highly allosterically regulated enzyme. BRD4592 binds at the TrpAB α–β-subunit interface and affects multiple steps in the enzyme's overall reaction, resulting in inhibition not easily overcome by changes in metabolic environment. We show that TrpAB is required for the survival of Mtb and Mycobacterium marinum in vivo and that this requirement may be independent of an adaptive immune response. This work highlights the effectiveness of allosteric inhibition for targeting proteins that are naturally highly dynamic and that are essential in vivo , despite their apparent dispensability under in vitro conditions, and suggests a framework for the discovery of a next generation of allosteric inhibitors.

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.The biological activities of >800,000 small molecules are represented within a uniform framework.