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With the ever-increasing number of synthesis-on-demand compounds for drug lead discovery, there is a great need for efficient search technologies. We present the successful application of a virtual screening method that combines two advances: (1) it avoids full library enumeration (2) products are evaluated by molecular docking, leveraging protein structural information. Crucially, these advances enable a structure-based technique that can efficiently explore libraries with billions of molecules and beyond. We apply this method to identify inhibitors of ROCK1 from almost one billion commercially available compounds. Out of 69 purchased compounds, 27 (39%) have K i values < 10 µM. X-ray structures of two leads confirm their docked poses. This approach to docking scales roughly with the number of reagents that span a chemical space and is therefore multiple orders of magnitude faster than traditional docking. Virtual screening of huge libraries is successful in identifying drug leads. Here, the authors describe a computational strategy, Chemical Space Docking, which combines docking with a reaction-based search of compounds, thereby enabling the exploration of billions of compounds and beyond.

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE in the chemical laboratory has evolved significantly in recent years, there remains a need for software solutions to navigate data-rich experiments. Here we have developed phactor™, a software that facilitates the performance and analysis of HTE in a chemical laboratory. phactor™ allows experimentalists to rapidly design arrays of chemical reactions or direct-to-biology experiments in 24, 96, 384, or 1,536 wellplates. Users can access online reagent data, such as a chemical inventory, to virtually populate wells with experiments and produce instructions to perform the reaction array manually, or with the assistance of a liquid handling robot. After completion of the reaction array, analytical results can be uploaded for facile evaluation, and to guide the next series of experiments. All chemical data, metadata, and results are stored in machine-readable formats that are readily translatable to various software. We also demonstrate the use of phactor™ in the discovery of several chemistries, including the identification of a low micromolar inhibitor of the SARS-CoV-2 main protease. Furthermore, phactor™ has been made available for free academic use in 24- and 96-well formats via an online interface. High-throughput experimentation is an increasingly important tool in reaction discovery, while there remains a need for software solutions to navigate data-rich experiments. Here the authors report phactor™, a software that facilitates the performance and analysis of high-throughput experimentation in a chemical laboratory.

Deubiquitinating enzymes (DUBs) are an emerging drug target class of ~100 proteases that cleave ubiquitin from protein substrates to regulate many cellular processes. A lack of selective chemical probes impedes pharmacologic interrogation of this important gene family. DUBs engage their cognate ligands through a myriad of interactions. We embrace this structural complexity to tailor a chemical diversification strategy for a DUB-focused covalent library. Pairing our library with activity-based protein profiling as a high-density primary screen, we identify selective hits against 23 endogenous DUBs spanning four subfamilies. Optimization of an azetidine hit yields a probe for the understudied DUB VCPIP1 with nanomolar potency and in-family selectivity. Our success in identifying good chemical starting points as well as structure-activity relationships across the gene family from a modest but purpose-build library challenges current paradigms that emphasize ultrahigh throughput in vitro or virtual screens against an ever-increasing scope of chemical space. Deubiquitinases (DUBs) are key signaling enzymes, many of which lack selective inhibitors. Chan et al . pair a DUB-focused covalent library to mass spectrometry activity-based protein profiling, leading to selective hits against 23 endogenous DUBs and a first-in-class VCPIP1 probe with nanomolar potency.

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting. Cellular nuisance compounds are a burden in chemical biology and drug screening. Here the authors profile prototypical cytotoxic and nuisance compounds using the cell painting assay to systematically characterise cellular morphologies associated with compound-dependent cellular injury and nuisance activity.

Targeting specific protein–protein interactions (PPIs) is an attractive concept for drug development, but hard to implement since intracellular antibodies do not penetrate cells and most small-molecule drugs are considered unsuitable for PPI inhibition. A potential solution to these problems is to select intracellular antibody fragments to block PPIs, use these antibody fragments for target validation in disease models and finally derive small molecules overlapping the antibody-binding site. Here, we explore this strategy using an anti-mutant RAS antibody fragment as a competitor in a small-molecule library screen for identifying RAS-binding compounds. The initial hits are optimized by structure-based design, resulting in potent RAS-binding compounds that interact with RAS inside the cells, prevent RAS-effector interactions and inhibit endogenous RAS-dependent signalling. Our results may aid RAS-dependent cancer drug development and demonstrate a general concept for developing small compounds to replace intracellular antibody fragments, enabling rational drug development to target validated PPIs. Intracellular antibodies can inhibit disease-relevant protein interactions, but inefficient cellular uptake limits their utility. Using a RAS-targeting intracellular antibody as a screening tool, the authors here identify small molecules that inhibit RAS-effector interactions and readily penetrate cells.

Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for GNN F and 0.958 for GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on pIC 50 with GNN F and GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Target engagement is a critical factor for therapeutic efficacy. Assessment of compound binding to native target proteins in live cells is therefore highly desirable in all stages of drug discovery. We report here the first compound library screen based on biophysical measurements of intracellular target binding, exemplified by human thymidylate synthase (TS). The screen selected accurately for all the tested known drugs acting on TS. We also identified TS inhibitors with novel chemistry and marketed drugs that were not previously known to target TS, including the DNA methyltransferase inhibitor decitabine. By following the cellular uptake and enzymatic conversion of known drugs we correlated the appearance of active metabolites over time with intracellular target engagement. These data distinguished a much slower activation of 5-fluorouracil when compared with nucleoside-based drugs. The approach establishes efficient means to associate drug uptake and activation with target binding during drug discovery. Drugs therapeutic efficacy relies on their capability of binding the relevant targets in a physiological environment, which has so far been hard to measure. Here, the authors present a compound library screen based on a target engagement assay that reports on protein stability upon ligands binding in cell.

In this study, pharmacophore based 3D QSAR models for human acetylcholinesterase (AChE) inhibitors were generated, with good significance, statistical values (r 2 training  = 0.73) and predictability (q 2 training  = 0.67). It was further validated by three methods (Fischer’s test, decoy set and Güner-Henry scoring method) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The criteria for virtual screening were also validated by testing the selective AChE inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed a novel and selective AChE inhibitor. Thus, the findings reported herein may provide a new strategy for the discovery of selective AChE inhibitors. The IC 50 value of compounds 5c and 6a presented selective inhibition of AChE without inhibiting butyrylcholinesterase (BChE) at uM level. Molecular docking studies were performed to explain the potent AChE inhibition of the target compounds studies to explain high affinity.

Many compounds with potentially reactive chemical motifs and poor physicochemical properties are published as selective modulators of biomolecules without sufficient validation and then propagated in the scientific literature as useful chemical probes. Several histone acetyltransferase (HAT) inhibitors with these liabilities are now routinely used to probe epigenetic pathways. We profile the most commonly used HAT inhibitors and confirm that the majority of them are nonselective interference compounds. Most (15 out of 23, 65%) of the inhibitors are flagged by ALARM NMR, an industry-developed counter-screen for promiscuous compounds. Biochemical counter-screens confirm that most of these compounds are either thiol-reactive or aggregators. Selectivity panels show many of these compounds modulate unrelated targets in vitro, while several also demonstrate nonspecific effects in cell assays. These data demonstrate the usefulness of performing counter-screens for bioassay promiscuity and assay interference, and raise caution about the utility of many widely used, but insufficiently validated, compounds employed in chemical biology. A substantial obstacle in basic research is the use of poorly validated tool compounds with purported useful biological functions. Here, the authors systematically profile widely used histone acetyltransferase inhibitors and find that the majority are nonselective interference compounds.

Tumour necrosis factor (TNF) is a cytokine belonging to a family of trimeric proteins; it has been shown to be a key mediator in autoimmune diseases such as rheumatoid arthritis and Crohn’s disease. While TNF is the target of several successful biologic drugs, attempts to design small molecule therapies directed to this cytokine have not led to approved products. Here we report the discovery of potent small molecule inhibitors of TNF that stabilise an asymmetrical form of the soluble TNF trimer, compromising signalling and inhibiting the functions of TNF in vitro and in vivo. This discovery paves the way for a class of small molecule drugs capable of modulating TNF function by stabilising a naturally sampled, receptor-incompetent conformation of TNF. Furthermore, this approach may prove to be a more general mechanism for inhibiting protein–protein interactions. While biologics have been successfully applied in TNF antagonist treatments, there are no clinically approved small molecules that target TNF. Here, the authors discover potent small molecule inhibitors of TNF, elucidate their molecular mechanism, and demonstrate TNF inhibition in vitro and in vivo.