ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Emerging and Re-emerging Warheads for Targeted Covalent Inhibitors: An Update

Covalent inhibitors and other types of covalent modalities have seen a revival in the past two decades, with a variety of new targeted covalent drugs having been approved in recent years. A key feature of such molecules is an intrinsically reactive group, typically a weak electrophile, which enables the irreversible or reversible formation of a covalent bond with a specific amino acid of the target protein. This reactive group, often called the "warhead", is a critical determinant of the ligand's activity, selectivity, and general biological properties. In 2019, we summarized emerging and re-emerging warhead chemistries to target cysteine and other amino acids (Gehringer, M.; Laufer, S. A. J. Med. Chem. 2019, 62, 5673-5724; DOI: 10.1021/acs.jmedchem.8b01153). Since then, the field has rapidly evolved. Here we discuss the progress on covalent warheads made since our last Perspective and their application in medicinal chemistry and chemical biology.

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

In vitro-in silico pharmacology and chemistry of Stercularin, isolated from Sterculia diversifolia

Stercularin is a coumarin, isolated from the ethyl acetate fraction of stem bark and leaves of S. diversifolia. Pharmacologically it is active against cancer, diabetes, and inflammation etc. The molecule is further screened for in vitro pharmacological activities. In addition, a detailed description on its drug likeness and pharmacokinetic profile has been established to further explore its fate as a drug candidate. Stercularin exhibited antiglycation, immunomodulatory, and leishmanicidal activity in three different in vitro models. The IC50 values obtained in these three assays were 80.22 ± 0.46 mg/ml, 12.8 ± 1.6 μg/ml, and 8.32 ± 0.42 μg/ml, respectively. In case of drug likeness evaluation, Stercularin has acceptable physicochemical properties and compliant with major drug likeness descriptors i.e., Lipinski rule, Pfizer rule, GSK rule, and "golden triangle". Accepting Lipinski rule implies the oral drug development of Stercularin. Pharmacokinetically, Stercularin is permeable to Caco-2 and MDCK cell lines. 'Boiled-egg' plot suggest intestinal route of absorption, blood brain barrier nonpermeating, and not affected by p-glycoprotein. Stercularin has high plasma protein binding with low free fraction circulating in the plasma. Stercularin proved to be the substrate and/or inhibitor of CYP 450 system with a moderate half-life and clearance rate to allow flexible dosing regimen. Finally, slight risk of toxicity exists for Stercularin, but not being limiting factors of drug knock out. A nature isolated Stercularin possess pharmacological activities and is predicted to have acceptable pharmacokinetic profile. Further drug development and in vivo studies are desirable for optimization.

Ultra-high-throughput mass spectrometry in drug discovery: fundamentals and recent advances

INTRODUCTION

Ultra-high-throughput mass spectrometry, uHT-MS, is a technology that utilizes ionization and sample delivery technologies optimized to enable sampling from well plates at > 1 sample per second. These technologies do not need a chromatographic separation step and can be utilized in a wide variety of assays to detect a broad range of analytes including small molecules, lipids, and proteins.

AREAS COVERED

This manuscript provides a brief historical review of high-throughput mass spectrometry and the recently developed technologies that have enabled uHT-MS. The report also provides examples and references on how uHT-MS has been used in biochemical and chemical assays, nuisance compound profiling, protein analysis and high throughput experimentation for chemical synthesis.

EXPERT OPINION

The fast analysis time provided by uHT-MS is transforming how biochemical and chemical assays are performed in drug discovery. The potential to associate phenotypic responses produced by 1000's of compound treatments with changes in endogenous metabolite and lipid signals is becoming feasible. With the augmentation of simple, fast, high-throughput sample preparation, the scope of uHT-MS usage will increase. However, it likely will not supplant LC-MS for analyses that require low detection limits from complex matrices or characterization of complex biotherapeutics such as antibody-drug conjugates.

The evolution of small molecule enzyme activators

There is a myriad of enzymes within the body responsible for maintaining homeostasis by providing the means to convert substrates to products as and when required. Physiological enzymes are tightly controlled by many signaling pathways and their products subsequently control other pathways. Traditionally, most drug discovery efforts focus on identifying enzyme inhibitors, due to upregulation being prevalent in many diseases and the existence of endogenous substrates that can be modified to afford inhibitor compounds. As enzyme downregulation and reduction of endogenous activators are observed in multiple diseases, the identification of small molecules with the ability to activate enzymes has recently entered the medicinal chemistry toolbox to afford chemical probes and potential therapeutics as an alternative means to intervene in diseases. In this review we highlight the progress made in the identification and advancement of non-kinase enzyme activators and their potential in treating various disease states.

Design, synthesis, and biological evaluation of carbonyl-hydrazine-1-carboxamide derivatives as anti-hepatic fibrosis agents targeting Nur77

Hepatic fibrosis remains a great challenge clinically. The orphan nuclear receptor Nur77 is recently suggested as the critical regulator of transforming growth factor-β (TGF-β) signaling, which plays a central role in multi-organic fibrosis. Herein, we optimized our previously reported Nur77-targeted compound 9 h for attempting to develop effective and safe anti-hepatic fibrosis agents. The critical pharmacophore scaffold of pyridine-carbonyl-hydrazine-1-carboxamide was retained, while the naphthalene ring was replaced with an aromatic ring containing pyridyl or indole groups. Four series of derivatives were thus generated, among which the compound 16f had excellent binding activity toward Nur77-LBD (KD = 470 nM) with the best inhibitory activity against the TGF- β 1 activation of hepatic stellate cells (HSCs) and low cytotoxicity to normal mice liver AML-12 cells (IC50 > 80 μM). In mice, 16f displayed potent activity against CCl4-induced liver fibrosis with improved liver function. Mechanistically, 16f-mediated inactivation of HSC and suppression of liver fibrosis were associated with its enhancement of autophagic flux in a Nur77-dependent manner. Together, 16f was identified as a potential anti-liver fibrosis agent. Our study suggests that Nur77 may serve as a critical anti-hepatic fibrosis target.

Unraveling the unexpected aggregation behavior of Pyrazole-Based compounds Targeting Mycobacterium tuberculosis UDP-Galactopyranose mutase

A pyrazole-based compound, MS208, was previously identified as an inhibitor of UDP-Galactopyranose Mutase from Mycobacterium tuberculosis (MtUGM). Targeting this enzyme is a novel therapeutic strategy for the development of new antituberculosis agents because MtUGM is an essential enzyme for the bacterial cell wall synthesis and it is not present in human. It was proposed that MS208 targets an allosteric site in MtUGM as MS208 followed a mixed inhibition model. DA10, an MS208 analogue, showed competitive inhibition rather than mixed inhibition. In this paper, we have used an integrated biophysical approach, including thermal shift assays, dynamic light scattering and nuclear magnetic resonance experiments, to show that MS208 and many analogues displayed unexpected aggregation behavior against MtUGM.

Heterocyclic chalcone (E)-1-(2-hydroxy-3,4,6-trimethoxyphenyl)-3-(thiophen-2-yl) prop-2-en-1-one derived from a natural product with antinociceptive, anti-inflammatory, and hypoglycemic effect in adult zebrafish

Diabetes is a disease linked to pathologies, such as chronic inflammation, neuropathy, and pain. The synthesis by the Claisen-Schmidt condensation reaction aims to obtain medium to high yield chalconic derivatives. Studies for the synthesis of new chalcone molecules aim at the structural manipulation of aromatic rings, as well as the replacement of rings by heterocycles, and combination through chemical reactions of synthesized structures with other molecules, in order to enhance biological activity. A chalcone was synthesized and evaluated for its antinociceptive, anti-inflammatory and hypoglycemic effect in adult zebrafish. In addition to reducing nociceptive behavior, chalcone (40 mg/kg) reversed post-treatment-induced acute and chronic hyperglycemia and reduced carrageenan-induced abdominal edema in zebrafish. It also showed an inhibitory effect on NO production in J774A.1 cells. When compared with the control groups, the oxidative stress generated after chronic hyperglycemia and after induction of abdominal edema was significantly reduced by chalcone. Molecular docking simulations of chalcone with Cox -1, Cox-2, and TRPA1 channel enzymes were performed and indicated that chalcone has a higher affinity for the COX-1 enzyme and 4 interactions with the TRPA1 channel. Chalcone also showed good pharmacokinetic properties as assessed by ADMET.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03696-8.

Targeted screening and identification of chlorhexidine as a pro-myogenic circadian clock activator

BACKGROUND

The circadian clock is an evolutionarily conserved mechanism that exerts pervasive temporal control in stem cell behavior. This time-keeping machinery is required for orchestrating myogenic progenitor properties in regenerative myogenesis that ameliorates muscular dystrophy. Here we report a screening platform to discover circadian clock modulators that promote myogenesis and identify chlorhexidine (CHX) as a clock-activating molecule with pro-myogenic activities.

METHODS

A high-throughput molecular docking pipeline was applied to identify compounds with a structural fit for a hydrophobic pocket within the key circadian transcription factor protein, Circadian Locomotor Output Cycles Kaput (CLOCK). These identified molecules were further screened for clock-modulatory activities and functional validations for pro-myogenic properties.

RESULTS

CHX was identified as a clock activator that promotes distinct aspects of myogenesis. CHX activated circadian clock that reduced cycling period length and augmented amplitude. This action was mediated by the targeted CLOCK structure via augmented interaction with heterodimer partner Bmal1, leading to enhanced CLOCK/Bmal1-controlled transcription with upregulation of core clock genes. Consistent with its clock-activating function, CHX displayed robust effects on stimulating myogenic differentiation in a clock-dependent manner. In addition, CHX augmented the proliferative and migratory activities of myoblasts.

CONCLUSION

Our findings demonstrate the feasibility of a screening platform to discover clock modulators with myogenic regulatory activities. Discovery of CHX as a pro-myogenic molecule could be applicable to promote regenerative capacities in ameliorating dystrophic or degenerative muscle diseases.

Passive Recording of Bioelectrical Signals from Non-Excitable Cells by Fluorescent Mirroring

Bioelectrical variations trigger different cell responses, including migration, mitosis, and mutation. At the tissue level, these actions result in phenomena such as wound healing, proliferation, and pathogenesis. Monitoring these mechanisms dynamically is highly desirable in diagnostics and drug testing. However, existing technologies are invasive: either they require physical access to the intracellular compartments, or they imply direct contact with the cellular medium. Here, we present a novel approach for the passive recording of electrical signals from non-excitable cells adhering to 3D microelectrodes, based on optical mirroring. Preliminary results yielded a fluorescence intensity output increase of the 5,8% in the presence of a HEK-293 cell on the electrode compared to bare microelectrodes. At present, this technology may be employed to evaluate cell-substrate adhesion and monitor cell proliferation. Further refinements could allow extrapolating quantitative data on surface charges and resting potential to investigate the electrical phenomena involved in cell migration and cancer progression.

Physicochemical Properties, Drug Likeness, ADMET, DFT Studies and in vitro antioxidant activity of Oxindole Derivatives

Poor pharmacokinetic and safety profiles create significant hurdles in the drug development process. This work focuses on a detailed understanding of drug discovery interplay among physicochemical, pharmacokinetic, toxicity endpoints, and antioxidant properties of oxindole derivatives. DFT compıutations were also performed at B3LYP/6-311G** level to evaluate the physicochemical properties, global reactivity features, and intramolecular interactions. The BOILED-Egg pharmacokinetic model envisaged gastrointestinal absorption, blood-brain barrier penetration, and no interaction with p-glycoprotein for compounds C1 and C2. The physicochemical evaluation revealed that C1 possesses superior drug-like properties fit for oral absorption. Both derivatives were predicted to have high plasma protein binding, efficient distribution, and inhibiting CYP 450 major isoforms but serve as substrates only for a few of them. Both molecules have mild to moderate clearance rates. Out of ten toxicity parameters, only hepatotoxicity was predicted. DFT results implied that the meta position of the -OH group made the possibility of charge transfer greater than -para positioned -OH, due to the ΔN_max (eV) values of molecules C1 and C2 being calculated at 2.596 and 2.477, respectively. Both C1 and C2 exhibited a concentration dependant DPPH and ABTS radical scavenging activity. The chemical structure-physicochemical-pharmacokinetic relationship identified the meta position as the favorite for the electron-withdrawing hydroxyl group. This provides useful insight to medicinal chemists to design 6-chlorooxindole derivatives with an acceptable drug-like and pharmacokinetic property.

Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors

Glutamate carboxypeptidase II (GCPII) is a metalloprotease implicated in neurological diseases and prostate oncology. While several classes of potent GCPII-specific inhibitors exist, the development of novel active scaffolds with different pharmacological profiles remains a challenge. Virtual screening followed by in vitro testing is an effective means for the discovery of novel active compounds. Structure- and ligand-based pharmacophore models were created based on a dataset of known GCPII-selective ligands. These models were used in a virtual screening of the SPECS compound library (∼209.000 compounds). Fifty top-scoring virtual hits were further experimentally tested for their ability to inhibit GCPII enzymatic activity in vitro. Six hits were found to have moderate to high inhibitory potency with the best virtual hit, a modified xanthene, inhibiting GCPII with an IC50 value of 353 ± 24 nM. The identification of this novel inhibitory scaffold illustrates the applicability of pharmacophore-based modeling for the discovery of GCPII-specific inhibitors.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Computational pharmacology and computational chemistry of 4-hydroxyisoleucine: Physicochemical, pharmacokinetic, and DFT-based approaches

Computational pharmacology and chemistry of drug-like properties along with pharmacokinetic studies have made it more amenable to decide or predict a potential drug candidate. 4-Hydroxyisoleucine is a pharmacologically active natural product with prominent antidiabetic properties. In this study, ADMETLab 2.0 was used to determine its important drug-related properties. 4-Hydroxyisoleucine is compliant with important drug-like physicochemical properties and pharma giants' drug-ability rules like Lipinski's, Pfizer, and GlaxoSmithKline (GSK) rules. Pharmacokinetically, it has been predicted to have satisfactory cell permeability. Blood-brain barrier permeation may add central nervous system (CNS) effects, while a very slight probability of being CYP2C9 substrate exists. None of the well-known toxicities were predicted in silico, being congruent with wet lab results, except for a "very slight risk" for respiratory toxicity predicted. The molecule is non ecotoxic as analyzed with common indicators such as bioconcentration and LC₅₀ for fathead minnow and daphnia magna. The toxicity parameters identified 4-hydroxyisoleucine as non-toxic to androgen receptors, PPAR-γ, mitochondrial membrane receptor, heat shock element, and p53. However, out of seven parameters, not even a single toxicophore was found. The density functional theory (DFT) study provided support to the findings obtained from drug-like property predictions. Hence, it is a very logical approach to proceed further with a detailed pharmacokinetics and drug development process for 4-hydroxyisoleucine.

Optimization of an in Silico Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference Compounds

Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

Inhibition of the CDK2 and Cyclin A complex leads to autophagic degradation of CDK2 in cancer cells

Cyclin-dependent kinase 2 (CDK2) complex is significantly over-activated in many cancers. While it makes CDK2 an attractive target for cancer therapy, most inhibitors against CDK2 are ATP competitors that are either nonspecific or highly toxic, and typically fail clinical trials. One alternative approach is to develop non-ATP competitive inhibitors; they disrupt interactions between CDK2 and either its partners or substrates, resulting in specific inhibition of CDK2 activities. In this report, we identify two potential druggable pockets located in the protein-protein interaction interface (PPI) between CDK2 and Cyclin A. To target the potential druggable pockets, we perform a LIVS in silico screening of a library containing 1925 FDA approved drugs. Using this approach, homoharringtonine (HHT) shows high affinity to the PPI and strongly disrupts the interaction between CDK2 and cyclins. Further, we demonstrate that HHT induces autophagic degradation of the CDK2 protein via tripartite motif 21 (Trim21) in cancer cells, which is confirmed in a leukemia mouse model and in human primary leukemia cells. These results thus identify an autophagic degradation mechanism of CDK2 protein and provide a potential avenue towards treating CDK2-dependent cancers.

CDK2 can drive the proliferation of cancer cells. Here, the authors screened for a non-ATP competitive inhibitor of the CDK2/cylinA complex and find that Homoharringtonine can disrupt the complex and promote the degradation of CDK2.

Redox active or thiol reactive? Optimization of rapid screens to identify less evident nuisance compounds

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity are routinely encountered in assays at various stages of drug discovery. We observed that assays for the investigation of thiol-reactive and redox-active compounds have not been collected in a comprehensive review. Here, we review these assays and subject them to experimental optimization to improve their reliability. We demonstrate the usefulness of our assay cascade by assaying a library of bioactive compounds, chemical probes, and a set of approved drugs. These high-throughput assays should complement the array of wet-lab and in silico assays during the initial stages of hit discovery campaigns to pursue only hit compounds with tractable mechanisms of action. Teaser: We provide an overview of assays to detect redox active and thiol reactive compounds and the robust protocols for identification of nuisance compounds during early stages of drug discovery programs.

Promoting GAINs (Give Attention to Limitations in Assays) over PAINs Alerts: no PAINS, more GAINs

Many concepts and guidelines in medicinal chemistry have been introduced to aid in successful drug discovery and development. An example is the concept of Pan-Assay Interference Compounds (PAINS) and the elimination of such nuisance compounds from high-throughput screening (HTS) libraries. PAINs, along with other guidelines in medicinal chemistry, are like double-edged swords. If used appropriately, they may be beneficial for drug discovery and development. However, rigid and blind use of such concepts can hinder productivity. In this perspective, we introduce GAINS (give attention to limitations in assays) and highlight its relevance for successful drug discovery.

Profiling MAP kinase cysteines for targeted covalent inhibitor design

Mitogen-activated protein kinases (MAPK) are important therapeutic targets, and yet no inhibitors have advanced to the market. Here we applied the GPU-accelerated continuous constant pH molecular dynamics (CpHMD) to calculate the pK a's and profile the cysteine reactivities of all 14 MAPKs for assisting the targeted covalent inhibitor design. The simulations not only recapitulated but also rationalized the reactive cysteines in the front pocket of JNK1/2/3 and the extended front pocket of p38α. Interestingly, the DFG - 1 cysteine in the DFG-in conformation of ERK1/ERK2 was found somewhat reactive or unreactive; however, simulations of MKK7 showed that switching to the DFG-out conformation makes the DFG - 1 cysteine reactive, suggesting the advantage of type II covalent inhibitors. Additionally, the simulations prospectively predicted several druggable cysteine and lysine sites, including the αH head cysteine in JNK1/3 and DFG + 6 cysteine in JNK2, corroborating the chemical proteomic screening data. Given the low cost and the ability to offer physics-based rationales, we envision CpHMD simulations to complement the chemo-proteomic platform for systematic profiling cysteine reactivities for targeted covalent drug discovery.

Discovery of novel drug-like antitubercular hits targeting the MEP pathway enzyme DXPS by strategic application of ligand-based virtual screening

In the present manuscript, we describe how we successfully used ligand-based virtual screening (LBVS) to identify two small-molecule, drug-like hit classes with excellent ADMET profiles against the difficult to address microbial enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS). In the fight against antimicrobial resistance (AMR), it has become increasingly important to address novel targets such as DXPS, the first enzyme of the 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway, which affords the universal isoprenoid precursors. This pathway is absent in humans but essential for pathogens such as Mycobacterium tuberculosis, making it a rich source of drug targets for the development of novel anti-infectives. Standard computer-aided drug-design tools, frequently applied in other areas of drug development, often fail for targets with large, hydrophilic binding sites such as DXPS. Therefore, we introduce the concept of pseudo-inhibitors, combining the benefits of pseudo-ligands (defining a pharmacophore) and pseudo-receptors (defining anchor points in the binding site), for providing the basis to perform a LBVS against M. tuberculosis DXPS. Starting from a diverse set of reference ligands showing weak inhibition of the orthologue from Deinococcus radiodurans DXPS, we identified three structurally unrelated classes with promising in vitro (against M. tuberculosis DXPS) and whole-cell activity including extensively drug-resistant strains of M. tuberculosis. The hits were validated to be specific inhibitors of DXPS and to have a unique mechanism of inhibition. Furthermore, two of the hits have a balanced profile in terms of metabolic and plasma stability and display a low frequency of resistance development, making them ideal starting points for hit-to-lead optimization of antibiotics with an unprecedented mode of action.

We identified two drug-like antitubercular hits with submicromolar inhibition constants against the target 1-deoxy-d-xylulose-5-phosphate synthase (DXPS) with a new mode of action and promising activity against drug-resistant tuberculosis.

A drug discovery toolbox for Nuclear Magnetic Resonance (NMR) characterization of ligands and their targets

Information about the structure, dynamics, and ligand-binding properties of biomolecules can be derived from Nuclear Magnetic Resonance (NMR) spectroscopy and provides valuable information for drug discovery. A multitude of experimental approaches provides a wealth of information that can be tailored to the system of interest. Methods to study the behavior of ligands upon target binding enable the identification of weak binders in a robust manner that is critical for the identification of truly novel binding interactions. This is particularly important for challenging targets. Observing the solution behavior of biomolecules yields information about their structure, dynamics, and interactions. This review describes the breadth of approaches that are available, many of which are under-utilized in a drug-discovery environment, and focuses on recent advances that continue to emerge.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

And Yet It Moves: Oxidation of the Nuclear Autoantigen La/SS-B Is the Driving Force for Nucleo-Cytoplasmic Shuttling

Decades ago, we and many other groups showed a nucleo-cytoplasmic translocation of La protein in cultured cells. This shuttling of La protein was seen after UV irradiation, virus infections, hydrogen peroxide exposure and the Fenton reaction based on iron or copper ions. All of these conditions are somehow related to oxidative stress. Unfortunately, these harsh conditions could also cause an artificial release of La protein. Even until today, the shuttling and the cytoplasmic function of La/SS-B is controversially discussed. Moreover, the driving mechanism for the shuttling of La protein remains unclear. Recently, we showed that La protein undergoes redox-dependent conformational changes. Moreover, we developed anti-La monoclonal antibodies (anti-La mAbs), which are specific for either the reduced form of La protein or the oxidized form. Using these tools, here we show that redox-dependent conformational changes are the driving force for the shuttling of La protein. Moreover, we show that translocation of La protein to the cytoplasm can be triggered in a ligand/receptor-dependent manner under physiological conditions. We show that ligands of toll-like receptors lead to a redox-dependent shuttling of La protein. The shuttling of La protein depends on the redox status of the respective cell type. Endothelial cells are usually resistant to the shuttling of La protein, while dendritic cells are highly sensitive. However, the deprivation of intracellular reducing agents in endothelial cells makes endothelial cells sensitive to a redox-dependent shuttling of La protein.

Targeting the non-ATP-binding pocket of the MAP kinase p38γ mediates a novel mechanism of cytotoxicity in cutaneous T-cell lymphoma (CTCL)

We describe here for the first time a lipid‐binding‐domain (LBD) in p38γ mitogen‐activated protein kinase (MAPK) involved in the response of T cells to a newly identified inhibitor, CSH71. We describe how CSH71, which binds to both the LBD and the ATP‐binding pocket of p38γ, is selectively cytotoxic to CTCL Hut78 cells but spares normal healthy peripheral blood mononuclear (PBMC) cells, and propose possible molecular mechanisms for its action. p38γ is a key player in CTCL development, and we expect that the ability to regulate its expression by specifically targeting the lipid‐binding domain will have important clinical relevance. Our findings characterize novel mechanisms of gene regulation in T lymphoma cells and validate the use of computational screening techniques to identify inhibitors for therapeutic development.

Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health Assay Guidance Manual Guidelines

Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS) Assay Guidance Manual (AGM) established an "Assay Artifacts and Interferences" section to address different sources of artifacts and interferences in biological assays. In addition to the frequent introduction of new chapters in this important topic area, older chapters are periodically updated by experts from academia, industry, and government to include new technologies and practices. Section chapters describe many best practices for mitigating and identifying compound-dependent assay interferences. Using two previously reported biochemical high-throughput screening campaigns for small-molecule inhibitors of the epigenetic targets Rtt109 and NSD2, the authors review best practices and direct readers to high-yield resources in the AGM and elsewhere for the mitigation and identification of compound-dependent reactivity and aggregation assay interferences.

Remodelin Is a Cryptic Assay Interference Chemotype That Does Not Inhibit NAT10-Dependent Cytidine Acetylation

Remodelin is a putative small molecule inhibitor of the RNA acetyltransferase NAT10 which has shown preclinical efficacy in models of the premature aging disease Hutchinson-Gilford Progeria Syndrome (HGPS). Here we evaluate remodelin's assay interference characteristics and effects on NAT10-catalyzed RNA cytidine acetylation. We find the remodelin chemotype constitutes a cryptic assay interference compound, which does not react with small molecule thiols but demonstrates protein reactivity in ALARM NMR and proteome-wide affinity profiling assays. Biophysical analyses find no direct evidence for interaction of remodelin with the NAT10 acetyltransferase active site. Cellular studies verify that N4-acetylcytidine (ac4C) is a nonredundant target of NAT10 activity in human cell lines and find that this RNA modification is not affected by remodelin treatment in several orthogonal assays. These studies display the potential for remodelin's chemotype to interact with multiple protein targets in cells and indicate remodelin should not be applied as a specific chemical inhibitor of NAT10-catalyzed RNA acetylation.

Discovery of a Potent and Selective Covalent p300/CBP Inhibitor

Aberrant gene activation driven by the histone acetyltransferases p300 and CREB binding protein (CBP) has been linked to several diseases, including cancers. Because of this, many efforts have been aimed toward the targeting of the closely related paralogues, p300 and CBP, but these endeavors have been exclusively directed toward noncovalent inhibitors. X-ray crystallography of A-485 revealed that both p300 and CBP possess a cysteine (C1450) near the active site, thus rendering covalent inhibition an attractive chemical approach. Herein we report the development of compound 2, an acrylamide-based inhibitor of p300/CBP that forms a covalent adduct with C1450. We demonstrated using mass spectrometry that compound 2 selectively targets C1450, and we also validated covalent binding using kinetics experiments and cellular washout studies. The discovery of covalent inhibitor 2 gives us a unique tool for the study of p300/CBP biology.

Active Components from Cassia abbreviata Prevent HIV-1 Entry by Distinct Mechanisms of Action

Cassia abbreviata is widely used in Sub-Saharan Africa for treating many diseases, including HIV-1 infection. We have recently described the chemical structures of 28 compounds isolated from an alcoholic crude extract of barks and roots of C. abbreviata, and showed that six bioactive compounds inhibit HIV-1 infection. In the present study, we demonstrate that the six compounds block HIV-1 entry into cells: oleanolic acid, palmitic acid, taxifolin, piceatannol, guibourtinidol-(4α→8)-epiafzelechin, and a novel compound named as cassiabrevone. We report, for the first time, that guibourtinidol-(4α→8)-epiafzelechin and cassiabrevone inhibit HIV-1 entry (IC₅₀ of 42.47 µM and 30.96 µM, respectively), as well as that piceatannol interacts with cellular membranes. Piceatannol inhibits HIV-1 infection in a dual-chamber assay mimicking the female genital tract, as well as HSV infection, emphasizing its potential as a microbicide. Structure-activity relationships (SAR) showed that pharmacophoric groups of piceatannol are strictly required to inhibit HIV-1 entry. By a ligand-based in silico study, we speculated that piceatannol and norartocarpetin may have a very similar mechanism of action and efficacy because of the highly comparable pharmacophoric and 3D space, while guibourtinidol-(4α→8)-epiafzelechin and cassiabrevone may display a different mechanism. We finally show that cassiabrevone plays a major role of the crude extract of CA by blocking the binding activity of HIV-1 gp120 and CD4.

Two Be or Not Two Be: The Nuclear Autoantigen La/SS-B Is Able to Form Dimers and Oligomers in a Redox Dependent Manner

According to the literature, the autoantigen La is involved in Cap-independent translation. It was proposed that one prerequisite for this function is the formation of a protein dimer. However, structural analyses argue against La protein dimers. Noteworthy to mention, these structural analyses were performed under reducing conditions. Here we describe that La protein can undergo redox-dependent structural changes. The oxidized form of La protein can form dimers, oligomers and even polymers stabilized by disulfide bridges. The primary sequence of La protein contains three cysteine residues. Only after mutation of all three cysteine residues to alanine La protein becomes insensitive to oxidation, indicating that all three cysteines are involved in redox-dependent structural changes. Biophysical analyses of the secondary structure of La protein support the redox-dependent conformational changes. Moreover, we identified monoclonal anti-La antibodies (anti-La mAbs) that react with either the reduced or oxidized form of La protein. Differential reactivities to the reduced and oxidized form of La protein were also found in anti-La sera of autoimmune patients.

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.

Luminescence feature of new 3,6-di(thiazolidin-5-one-2-yl)-carbazole derivative: synthesis, photophysical properties, density functional theory studies, and crystal shape effect

A new carbazole chromophore conjugated with substituted thiazolidine-4-one (CzPT) was synthesized by applying the Knoevenagel reaction between 3,6-diformyl-N-hexylcarbazole and ethyl 2-aceto-2-(5-oxo-3-phenylthiazolidin-2-ylidene)acetate. The chemical structure of the new derivative (CzPT) was elucidated by spectral studies. The CzPT absorption spectra in different solvents exhibited a red shift for λ_max by increasing solvent polarity. Bands at 430-474 nm appeared and were attributed to intramolecular charge transfer with high π-π* characteristics. CzPT fluorescence spectra exhibited a red shift after increasing the solvent polarity. To understand the Stokes' shift ( ∆ ν ¯ ) behaviour of the CzPT derivative referring to the polarity of solvents, Lippert-Mataga and linear solvation-energy relationship (LSER) models were employed in which the LSER exhibited respectable results compared with Lippert-Mataga (r² = 0.9707). Moreover, time-dependent density functional theory absorption spectra in hexane and dimethylformamide showed that λ_max had a major contribution in the highest occupied molecular orbital to lowest unoccupied molecular orbital transition in both solvents. In addition, the reduced uniformity of crystal features may lead to dislocation or anomalous arrangement of crystals with irregular spacing, which automatically enhances the optical properties of such crystals.

Synthesis of Guaianolide Analogues with a Tunable α-Methylene-γ-lactam Electrophile and Correlating Bioactivity with Thiol Reactivity

α-Methylene-γ-lactones are present in ∼3% of known natural products, and compounds comprising this motif display a range of biological activities. However, this reactive lactone limits informed structure-activity relationships for these bioactive molecules. Herein, we describe chemically tuning the electrophilicity of the α-methylene-γ-lactone by replacement with an α-methylene-γ-lactam. Guaianolide analogues having α-methylene-γ-lactams are synthesized using the allenic Pauson-Khand reaction. Substitution of the lactam nitrogen with electronically different groups affords diverse thiol reactivity. Cellular NF-κB inhibition assays for these lactams were benchmarked against parthenolide and a synthetic α-methylene-γ-lactone showing a positive correlation between thiol reactivity and bioactivity. Cytotoxicity assays show good correlation at the outer limits of thiol reactivity but less so for compounds with intermediate reactivity. A La assay to detect reactive molecules by nuclear magnetic resonance and mass spectrometry peptide sequencing assays with the La antigen protein demonstrate that lactam analogues with muted nonspecific thiol reactivities constitute a better electrophile for rational chemical probe and therapeutic molecule design.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds

BACKGROUND

Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection.

CONCLUSION

ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

AI26 inhibits the ADP-ribosylhydrolase ARH3 and suppresses DNA damage repair

The ADP-ribosylhydrolase ARH3 plays a key role in DNA damage repair, digesting poly(ADP-ribose) and removing ADP-ribose from serine residues of the substrates. Specific inhibitors that selectively target ARH3 would be a useful tool to examine DNA damage repair, as well as a possible strategy for tumor suppression. However, efforts to date have not identified any suitable compounds. Here, we used in silico and biochemistry screening to search for ARH3 inhibitors. We discovered a small molecule compound named ARH3 inhibitor 26 (AI26) as, to our knowledge, the first ARH3 inhibitor. AI26 binds to the catalytic pocket of ARH3 and inhibits the enzymatic activity of ARH3 with an estimated IC₅₀ of ∼2.41 μm in vitro Moreover, hydrolysis of DNA damage-induced ADP-ribosylation was clearly inhibited when cells were pretreated with AI26, leading to defects in DNA damage repair. In addition, tumor cells with DNA damage repair defects were hypersensitive to AI26 treatment, as well as combinations of AI26 and other DNA-damaging agents such as camptothecin and doxorubicin. Collectively, these results reveal not only a chemical probe to study ARH3-mediated DNA damage repair but also a chemotherapeutic strategy for tumor suppression.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

Targeting FTO Suppresses Cancer Stem Cell Maintenance and Immune Evasion

Fat mass and obesity-associated protein (FTO), an RNA N⁶-methyladenosine (m⁶A) demethylase, plays oncogenic roles in various cancers, presenting an opportunity for the development of effective targeted therapeutics. Here, we report two potent small-molecule FTO inhibitors that exhibit strong anti-tumor effects in multiple types of cancers. We show that genetic depletion and pharmacological inhibition of FTO dramatically attenuate leukemia stem/initiating cell self-renewal and reprogram immune response by suppressing expression of immune checkpoint genes, especially LILRB4. FTO inhibition sensitizes leukemia cells to T cell cytotoxicity and overcomes hypomethylating agent-induced immune evasion. Our study demonstrates that FTO plays critical roles in cancer stem cell self-renewal and immune evasion and highlights the broad potential of targeting FTO for cancer therapy.

Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays

Luciferase-based bioluminescence detection techniques are highly favored in high-throughput screening (HTS), in which the firefly luciferase (FLuc) is the most commonly used variant. However, FLuc inhibitors can interfere with the activity of luciferase, which may result in false positive signals in HTS assays. In order to reduce the unnecessary cost of time and money, an in silico prediction model for FLuc inhibitors is highly desirable. In this study, we built an extensive data set consisting of 20 888 FLuc inhibitors and 198 608 noninhibitors, and then developed a group of classification models based on the combination of three machine learning (ML) algorithms and four types of molecular representations. The best prediction model based on XGBoost and ECFP4 and MOE2d descriptors yielded a balanced accuracy (BA) of 0.878 and an area under the receiver operating characteristic curve (AUC) value of 0.958 for the validation set, and a BA of 0.886 and an AUC of 0.947 for the test set. Three external validation sets, including set 1 (3231 FLuc inhibitors and 69 783 noninhibitors), set 2 (695 FLuc inhibitors and 75 913 noninhibitors), and set 3 (1138 FLuc inhibitors and 8155 noninhibitors), were used to verify the predictive ability of our models. The BA values for the three external validation sets given by the best model are 0.864, 0.845, and 0.791, respectively. In addition, the important features or structural fragments related to FLuc inhibitors were recognized by the Shapley additive explanations (SHAP) method along with their influences on predictions, which may provide valuable clues to detecting undesirable luciferase inhibitors. Based on the important and explanatory features, 16 rules were proposed for detecting FLuc inhibitors, which can achieve a correction rate of 70% for FLuc inhibitors. Furthermore, a comparison with existing prediction rules and models for FLuc inhibitors used in virtual screening verified the high reliability of the models and rules proposed in this study. We also used the model to screen three curated chemical databases, and almost 10% of the molecules in the evaluated databases were predicted as inhibitors, highlighting the potential risk of false positives in luciferase-based assays. Finally, a public web server called ChemFLuc was developed (http://admet.scbdd.com/chemfluc/index/), and it offers a free available service to predict potential FLuc inhibitors.

Development of Benzenesulfonamide Derivatives as Potent Glutathione Transferase Omega-1 Inhibitors

Glutathione transferase omega-1 (GSTO1-1) is an enzyme whose function supports the activation of interleukin (IL)-1β and IL-18 that are implicated in a variety of inflammatory disease states for which small-molecule inhibitors are sought. The potent reactivity of the active-site cysteine has resulted in reported inhibitors that act by covalent labeling. In this study, structure-activity relationship (SAR) elaboration of the reported GSTO1-1 inhibitor C1-27 was undertaken. Compounds were evaluated for inhibitory activity toward purified recombinant GSTO1-1 and for indicators of target engagement in cell-based assays. As covalent inhibitors, the kinact/KI values of selected compounds were determined, as well as in vivo pharmacokinetics analysis. Cocrystal structures of key novel compounds in complex with GSTO1-1 were also solved. This study represents the first application of a biochemical assay for GSTO1-1 to determine kinact/KI values for tested inhibitors and the most extensive set of cell-based data for a GSTO1-1 inhibitor SAR series reported to date. Our research culminated in the discovery of 25, which we propose as the preferred biochemical tool to interrogate cellular responses to GSTO1-1 inhibition.

Discovery of Acylsulfonohydrazide-Derived Inhibitors of the Lysine Acetyltransferase, KAT6A, as Potent Senescence-Inducing Anti-Cancer Agents

A high-throughput screen designed to discover new inhibitors of histone acetyltransferase KAT6A uncovered CTX-0124143 (1), a unique aryl acylsulfonohydrazide with an IC₅₀ of 1.0 μM. Using this acylsulfonohydrazide as a template, we herein disclose the results of our extensive structure-activity relationship investigations, which resulted in the discovery of advanced compounds such as 55 and 80. These two compounds represent significant improvements on our recently reported prototypical lead WM-8014 (3) as they are not only equivalently potent as inhibitors of KAT6A but are less lipophilic and significantly more stable to microsomal degradation. Furthermore, during this process, we discovered a distinct structural subclass that contains key 2-fluorobenzenesulfonyl and phenylpyridine motifs, culminating in the discovery of WM-1119 (4). This compound is a highly potent KAT6A inhibitor (IC₅₀ = 6.3 nM; K_D = 0.002 μM), competes with Ac-CoA by binding to the Ac-CoA binding site, and has an oral bioavailability of 56% in rats.

Epigenetic drug discovery: systematic assessment of chemical space

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 10⁶ ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 10⁶ compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.