Curcumin interferes with chitin synthesis in Aedes aegypti: a computational and experimental investigation.

Throughout history, vector-borne diseases have consistently posed significant challenges to human health. Among the strategies for vector control, chemical insecticides have seen widespread use since their inception. Nevertheless, their effectiveness is continually undermined by the steady growth of insecticide resistance within these vector populations. As such, the demand for more robust, efficient, and cost-effective natural insecticides has become increasingly pressing. One promising avenue of research focuses on chitin, a crucial structural component of mosquitoes' exoskeletons and other insects. Chitin not only provides protection and rigidity but also lends flexibility to the insect body. It undergoes substantial transformations during insect molting, a process known as ecdysis. Crucially, the production of chitin is facilitated by an enzyme known as chitin synthase, making it an attractive target for potential novel insecticides. Our recent study delved into the impacts of curcumin, a natural derivative of turmeric, on chitin synthesis and larval development in Aedes aegypti, a mosquito species known to transmit dengue and yellow fever. Our findings demonstrate that even sub-lethal amounts of curcumin can significantly reduce overall chitin content and disrupt the cuticle development in the 4th instar larvae of Aedes aegypti. Further to this, we utilized computational analyses to investigate how curcumin interacts with chitin synthase. Techniques such as molecular docking, pharmacophore feature mapping, and molecular dynamics (MD) simulations helped to illustrate that curcumin binds to the same site as polyoxin D, a recognized inhibitor of chitin synthase. These findings point to curcumin's potential as a natural, bioactive larvicide that targets chitin synthase in mosquitoes and potentially other insects.

In Silico Methods for the Discovery of Orthosteric GABAB Receptor Compounds.

The GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor comprised of the GABAB1a/b and GABAB2 subunits. The endogenous orthosteric agonist γ-amino-butyric acid (GABA) binds within the extracellular Venus flytrap (VFT) domain of the GABAB1a/b subunit. The receptor is associated with numerous neurological and neuropsychiatric disorders including learning and memory deficits, depression and anxiety, addiction and epilepsy, and is an interesting target for new drug development. Ligand- and structure-based virtual screening (VS) are used to identify hits in preclinical drug discovery. In the present study, we have evaluated classical ligand-based in silico methods, fingerprinting and pharmacophore mapping and structure-based in silico methods, structure-based pharmacophores, docking and scoring, and linear interaction approximation (LIA) for their aptitude to identify orthosteric GABAB-R compounds. Our results show that the limited number of active compounds and their high structural similarity complicate the use of ligand-based methods. However, by combining ligand-based methods with different structure-based methods active compounds were identified in front of DUDE-E decoys and the number of false positives was reduced, indicating that novel orthosteric GABAB-R compounds may be identified by a combination of ligand-based and structure-based in silico methods.

Combined Virtual and Experimental Screening for CK1 Inhibitors Identifies a Modulator of p53 and Reveals Important Aspects of in Silico Screening Performance.

A compound collection of pronounced structural diversity was comprehensively screened for inhibitors of the DNA damage-related kinase CK1. The collection was evaluated in vitro. A potent and selective CK1 inhibitor was discovered and its capacity to modulate the endogenous levels of the CK1-regulated tumor suppressor p53 was demonstrated in cancer cell lines. Administration of 10 µM of the compound resulted in significant increase of p53 levels, reaching almost 2-fold in hepatocellular carcinoma cells. In parallel to experimental screening, two representative and orthogonal in silico screening methodologies were implemented for enabling the retrospective assessment of virtual screening performance on a case-specific basis. Results showed that both techniques performed at an acceptable and fairly comparable level, with a slight advantage of the structure-based over the ligand-based approach. However, both approaches demonstrated notable sensitivity upon parameters such as screening template choice and treatment of redundancy in the enumerated compound collection. An effort to combine insight derived by sequential implementation of the two methods afforded poor further improvement of screening performance. Overall, the presented assessment highlights the relation between improper use of enrichment metrics and misleading results, and demonstrates the inherent delicacy of in silico methods, emphasizing the challenging character of virtual screening protocol optimization.

DeCAF-Discrimination, Comparison, Alignment Tool for 2D PHarmacophores.

Comparison of small molecules is a common component of many cheminformatics workflows, including the design of new compounds and libraries as well as side-effect predictions and drug repurposing. Currently, large-scale comparison methods rely mostly on simple fingerprint representation of molecules, which take into account the structural similarities of compounds. Methods that utilize 3D information depend on multiple conformer generation steps, which are computationally expensive and can greatly influence their results. The aim of this study was to augment molecule representation with spatial and physicochemical properties while simultaneously avoiding conformer generation. To achieve this goal, we describe a molecule as an undirected graph in which the nodes correspond to atoms with pharmacophoric properties and the edges of the graph represent the distances between features. This approach combines the benefits of a conformation-free representation of a molecule with additional spatial information. We implemented our approach as an open-source Python module called DeCAF (Discrimination, Comparison, Alignment tool for 2D PHarmacophores), freely available at http://bitbucket.org/marta-sd/decaf. We show DeCAF's strengths and weaknesses with usage examples and thorough statistical evaluation. Additionally, we show that our method can be manually tweaked to further improve the results for specific tasks. The full dataset on which DeCAF was evaluated and all scripts used to calculate and analyze the results are also provided.

Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties.

Diabetes mellitus (DM) poses a major health problem, for which there is an unmet need to develop novel drugs. The application of in silico techniques and optimization algorithms is instrumental to achieving this goal. A set of 97 approved anti-diabetic drugs, representing the active domain, and a set of 2892 natural products, representing the inactive domain, were used to construct predictive models and to index anti-diabetic bioactivity. Our recently-developed approach of 'iterative stochastic elimination' was utilized. This article describes a highly discriminative and robust model, with an area under the curve above 0.96. Using the indexing model and a mix ratio of 1:1000 (active/inactive), 65% of the anti-diabetic drugs in the sample were captured in the top 1% of the screened compounds, compared to 1% in the random model. Some of the natural products that scored highly as potential anti-diabetic drug candidates are disclosed. One of those natural products is caffeine, which is noted in the scientific literature as having the capability to decrease blood glucose levels. The other nine phytochemicals await evaluation in a wet lab for their anti-diabetic activity. The indexing model proposed herein is useful for the virtual screening of large chemical databases and for the construction of anti-diabetes focused libraries.

Ligand-based virtual screening identifies a family of selective cannabinoid receptor 2 agonists.

The cannabinoid receptor 2 (CB2R) has been linked with the regulation of inflammation, and selective receptor activation has been proposed as a target for the treatment of a range of inflammatory diseases such as atherosclerosis and arthritis. In order to identify selective CB2R agonists with appropriate physicochemical and ADME properties for future evaluation in vivo, we first performed a ligand-based virtual screen. Subsequent medicinal chemistry optimisation studies led to the identification of a new class of selective CB2R agonists. Several examples showed high levels of activity (EC50<200 nM) and binding affinity (Ki<200 nM) for the CB2R, and no detectable activity at the CB1R. The most promising example, DIAS2, also showed favourable in vitro metabolic stability and absorption properties along with a clean selectivity profile when evaluated against a panel of GPCRs and kinases.

Synthesis and anticancer cell potential of steroidal 16,17-seco-16,17a-dinitriles: identification of a selective inhibitor of hormone-independent breast cancer cells.

We report the synthesis of steroidal 16,17-seco-16,17a-dinitriles and investigate their antitumor cell properties. Compounds were evaluated for anticancer potential by in vitro antiproliferation studies, molecular docking and virtual screening. Several compounds inhibit the growth of breast and prostate cancer cell lines (MCF-7, MDA-MB-231 and PC3), and/or cervical cancer cells (HeLa). Supporting this, molecular docking predicts that steroidal 16,17-seco-16,17a-dinitriles could bind with high affinity to multiple molecular targets of breast and prostate cancer treatment (aromatase, estrogen receptor α, androgen receptor and 17α-hydroxylase) facilitated by D-seco flexibility and nitrile-mediated contacts. Thus, 16,17-seco-16,17a-dinitriles may be useful for the design of inhibitors of multiple steroidogenesis pathways. Strikingly, 10, a 1,4-dien-3-on derivative, displayed selective submicromolar antiproliferative activity against hormone-dependent (MCF-7) and -independent (MDA-MB-231) breast cancer cells (IC50 0.52, 0.11µM, respectively). Ligand-based 3D similarity searches suggest AKR1C, 17ß-HSD and/or 3ß-HSD subfamilies as responsible for this antiproliferative activity, while fast molecular docking identified AKR1C and ERß as potential binders-both targets in the treatment of hormone-independent breast cancers.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior.

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Structure and ligand-based drug discovery of IL-4 inhibitors via interaction-energy-based learning approaches.

Interleukin-4 (IL-4), an anti-inflammatory cytokine plays significant in the development of various diseases especially asthmatic allergies. Previous structural and functional studies of IL-4 with its receptor bring forth different types of inhibitors to block their interaction but each of them failed in clinical trials. Since, no synthetic molecules have been identified against IL-4, so far. Therefore, 21 in-house tested IL-4 inhibitors were blindly docked over the entire surface of IL-4 to predict a suitable and druggable binding site as the crystal structure of IL-4 protein in complex with ligand has not been reported yet. After binding site prediction, both ligand-based and structure-based pharmacophore were generated to screen three ZINC libraries (24.5 M) i.e. purchasable, natural product and natural derivative. A total 5,800 top-scored compounds were further subjected towards score-based screening to find the potential leads. Following protein-ligand interaction fingerprints (PLIF) and molecular visualization of selected hits, six top-scored compounds (five from purchasable and one from natural product library) were further moved towards their stability dynamics, followed by their absolute binding free energy and residue-based energy decomposition calculation by MM-GBSA method. These efforts help us to reveal the key factors responsible for ligand binding that might help to improve the binding and stability of these newly discovered hits by structural modifications.Communicated by Freddie R. Salsbury.

Valorisation of stachysetin from cultivated Stachys iva Griseb. as anti-diabetic agent: a multi-spectroscopic and molecular docking approach.

Stachys species are considered as important medicinal plants with numerous health benefit effects. In continuation of our research on the Greek Stachys species, the chemical profile of the aerial parts of cultivated S. iva Griseb. has been explored. The NMR profiles of the plant extract/infusion were used to guide the isolation process, leading to the targeted isolation of seventeen known compounds. The rare acylated flavonoid, stachysetin, was isolated for the third time from plant species in the international literature. Identification of the characteristic signals of stachysetin in the 1D 1H-NMR spectrum of the crude extract was presented. In order to evaluate the potential of the identified chemical space in Stachys to bear possible bioactivity against diabetes, we performed an in silico screening against 17 proteins implicated in diabetes, as also ligand based similarity metrics against established anti-diabetic drugs. The results capitalized the anti-diabetic potency of stachysetin. Its binding profile to the major drug carrier plasma protein serum albumin was also explored along with its photophysical properties suggesting that stachysetin could be recognized and delivered in plasma through serum albumin and also could be tracked through near-infrared imaging. Communicated by Ramaswamy H. Sarma.

Virtual Screening for Potential Inhibitors of Human Hexokinase II for the Development of Anti-Dengue Therapeutics.

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human's body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Human hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Alpha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski's Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of -7.2, -7.0, and -6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of -7.8, -7.4, and -7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of -6.8, -6.3, and -6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we proposed novel compounds, which may act as potential drugs against DENV by inhibiting HKII's activity.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review.

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as 'dark chemical space' or 'dark chemistry.' Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: 'genomics', proteomics and 'in-silico simulation' will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Indexing Natural Products for their Antifungal Activity by Filters-based Approach: Disclosure of Discriminative Properties.

BACKGROUND: A considerable worldwide increase in the rate of invasive fungal infections and resistance toward antifungal drugs was witnessed during the past few decades. Therefore, the need for newer antifungal candidates is paramount. Nature has been the core source of therapeutics for thousands of years, and an impressive number of modern drugs including antifungals were derived from natural sources. In order to facilitate the recognition of potential candidates that can be derived from natural sources, an iterative stochastic elimination optimization technique to index natural products for their antifungal activity was utilized. METHODS: A set of 240 FDA-approved antifungal drugs, which represent the active domain, and a set of 2,892 natural products, which represent the inactive domain, were used to construct predictive models and to index natural products for their antifungal bioactivity. The area under the curve for the produced predictive model was 0.89. When applying it to a database that is composed of active/inactive chemicals, we succeeded to detect 42% of the actives (antifungal drugs) in the top one percent of the screened chemicals, compared with one-percent when using a random model. RESULTS AND CONCLUSION: Eight natural products, which were highly scored as likely antifungal drugs, are disclosed. Searching PubMed showed only one molecule (Flindersine) out of the eight that have been tested was reported as an antifungal. The other seven phytochemicals await evaluation for their antifungal bioactivity in a wet laboratory.

Rational drug repurposing for alzheimer's treatment using in-silico ligand and structure-based approaches

Abstract Alzheimer's disease is a devastating neurodegenerative disorder characterized by memory loss and cognitive decline. New AD treatments are essential, and drug repositioning is a promising approach. In this study, we combined ligand-based and structure-based approaches to identify potential candidates among FDA-approved drugs for AD treatment. We used the human acetylcholinesterase receptor structure (PDB ID: 4EY7) and applied Rapid Overlay of Chemical Structures and Swiss Similarity for ligand-based screening.Computational shape-based screening revealed 20 out of 760 FDA approved drugs with promising structural similarity to Donepezil, an AD treatment AChE inhibitor and query molecule. The screened hits were further analyzed using docking analysis with Autodock Vina and Schrodinger glide. Predicted binding affinities of hits to AChE receptor guided prioritization of potential drug candidates. Doxazosin, Oxypertine, Cyclopenthiazide, Mestranol, and Terazosin exhibited favorable properties in shape similarity, docking energy, and molecular dynamics stability.Molecular dynamics simulations confirmed the stability of the complexes over 100 ns. Binding free energy analysis using MM-GBSA indicated favourable binding energies for the selected drugs. ADME, formulation studies offered insights into therapeutic applications and predicted toxicity.This comprehensive computational approach identified potential FDA-approved drugs (especially Doxazosin) as candidates for repurposing in AD treatment, warranting further investigation and clinical assessment.

Applications of Dissolution-DNP for NMR Screening.

Experimental screening for protein-ligand interactions is a central task in drug discovery. Nuclear magnetic resonance (NMR) spectroscopy enables the determination of binding affinities, as well as the measurement of structural and dynamic parameters governing the interaction. With traditional liquid-state NMR relying on a nuclear spin polarization on the order of 10-5, hyperpolarization methods such as dissolution dynamic nuclear polarization (D-DNP) can increase signals by several orders of magnitude. The resulting increase in sensitivity has the potential to reduce requirements for the concentration of protein and ligands, improve the accuracy of the detection of interaction by allowing the use of near-stoichiometric conditions, and increase throughput. This chapter introduces a selection of basic techniques for the application of D-DNP to screening. Procedures for hyperpolarization are briefly reviewed, followed by the description of NMR methods for detection of binding through changes in chemical shift and relaxation parameters. Experiments employing competitive binding with a known ligand are shown, which can be used to determine binding affinity or yield structural information on the pharmacophore. The specific challenges of working with nonrenewable hyperpolarization are reviewed, and solutions including the use of multiplexed NMR detection are described. Altogether, the methods summarized in this chapter are intended to allow for the efficient detection of binding affinity, structure, and dynamics facilitated through substantial signal enhancements provided by hyperpolarization.

Automated Inference of Chemical Discriminants of Biological Activity.

Ligand-based virtual screening has become a standard technique for the efficient discovery of bioactive small molecules. Following assays to determine the activity of compounds selected by virtual screening, or other approaches in which dozens to thousands of molecules have been tested, machine learning techniques make it straightforward to discover the patterns of chemical groups that correlate with the desired biological activity. Defining the chemical features that generate activity can be used to guide the selection of molecules for subsequent rounds of screening and assaying, as well as help design new, more active molecules for organic synthesis.The quantitative structure-activity relationship machine learning protocols we describe here, using decision trees, random forests, and sequential feature selection, take as input the chemical structure of a single, known active small molecule (e.g., an inhibitor, agonist, or substrate) for comparison with the structure of each tested molecule. Knowledge of the atomic structure of the protein target and its interactions with the active compound are not required. These protocols can be modified and applied to any data set that consists of a series of measured structural, chemical, or other features for each tested molecule, along with the experimentally measured value of the response variable you would like to predict or optimize for your project, for instance, inhibitory activity in a biological assay or ΔGbinding. To illustrate the use of different machine learning algorithms, we step through the analysis of a dataset of inhibitor candidates from virtual screening that were tested recently for their ability to inhibit GPCR-mediated signaling in a vertebrate.

In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies.

A number of diverse approaches for efficient screening of compound collections in silico are nowadays available, each with their own methodological background, successes and limitations. Implementation of such virtual screening methods has enabled an impressive acceleration in the search toward the most biologically relevant regions of chemical space and has greatly facilitated the discovery of novel biologically active molecules. It is noteworthy that the range of principles on which the available virtual screening methodologies are based is wide enough for several of these methods to be considered as orthogonal to a good extent. We hereby propose a simple and extensible protocol aiming at integrating the diverse information derived by such virtual screening methods in a consensus manner that can achieve an improvement of the hit rate obtained by individual use of those methods. The protocol can be performed in its basic version as described in this work, but it can also be extended manually by integrating a number of different screening tools and their case-specific variations to further increase the performance of virtual screening in prioritizing the most promising compounds for in vitro evaluations.

Current Scenario in Structure and Ligand-Based Drug Design on Anti-colon Cancer Drugs.

Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field and the current scenario in drug designing of cancer drugs are discussed. This review provides information on how cancer drugs were formulated and identified using computational power by the drug discovery society.

Overview of Methods and Strategies for Conducting Virtual Small Molecule Screening.

Virtual screening (VS) in the context of drug discovery is the use of computational methods to discover novel ligands with a desired biological activity from within a larger collection of molecules. These techniques have been in use for many years, there is a wide range of methodologies available, and many successful applications have been reported in the literature. VS is often used as an alternative or a complement to High-throughput screening (HTS) or other methods to identify ligands for target validation or medicinal chemistry projects. This unit does not present an exhaustive review of available methods, or document specific instructions on use of individual software packages. Rather, a general overview of the methods available are presented and general strategies are described for VS based on accepted practices and the authors' experience as computational chemists in an industrial research laboratory. First, the most common methods available for VS are reviewed, categorized as either receptor- or ligand-based. Subsequently, strategic considerations are presented for choosing a VS method, or a combination of methods, as well as the necessary steps to prepare, run, and analyze a VS campaign. © 2017 by John Wiley & Sons, Inc.

Tandem virtual screening targeting the SRA domain of UHRF1 identifies a novel chemical tool modulating DNA methylation.

Ubiquitin-like protein UHRF1 that contains PHD and RING finger domain 1 is a key epigenetic protein enabling maintenance of the DNA methylation status through replication. A tandem virtual screening approach was implemented for identifying small molecules able to bind the 5-methylcytosine pocket of UHRF1 and inhibit its functionality. The NCI/DTP small molecules Repository was screened in silico by a combined protocol implementing structure-based and ligand-based methodologies. Consensus ranking was utilized to select a set of 27 top-ranked compounds that were subsequently evaluated experimentally in a stepwise manner for their ability to demethylate DNA in cellulo using PCR-MS and HPLC-MS/MS. The most active molecules were further assessed in a cell-based setting by the Proximity Ligation In Situ Assay and the ApoTome technology. Both evaluations confirmed that the DNMT1/UHRF1 interactions were significantly reduced after 4 h of incubation of U251 glioma cells with the most potent compound NSC232003, showing a 50% interaction inhibition at 15 µM as well as induction of global DNA cytosine demethylation as measured by ELISA. This is the first report of a chemical tool that targets UHRF1 and modulates DNA methylation in a cell context by potentially disrupting DNMT1/UHRF1 interactions. Compound NSC232003, a uracil derivative freely available by the NCI/DTP Repository, provides a versatile lead for developing highly potent and cell-permeable UHRF1 inhibitors that will enable dissection of DNA methylation inheritance.