Structure-based classification predicts drug response in EGFR-mutant NSCLC.

Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18-21 and are established driver mutations in non-small cell lung cancer (NSCLC)1-3. Targeted therapies are approved for patients with 'classical' mutations and a small number of other mutations4-6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7-10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure-function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure-function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid.

We have identified novel HIV-1 capsid inhibitors targeting the PF74 binding site. Acting as the building block of the HIV-1 capsid core, the HIV-1 capsid protein plays an important role in the viral life cycle and is an attractive target for antiviral development. A structure-based virtual screening workflow for hit identification was employed, which includes docking 1.6 million commercially-available drug-like compounds from the ZINC database to the capsid dimer, followed by applying two absolute binding free energy (ABFE) filters on the 500 top-ranked molecules from docking. The first employs the Binding Energy Distribution Analysis Method (BEDAM) in implicit solvent. The top-ranked compounds are then refined using the Double Decoupling method in explicit solvent. Both docking and BEDAM refinement were carried out on the IBM World Community Grid as part of the FightAIDS@Home project. Using this virtual screening workflow, we identified 24 molecules with calculated binding free energies between - 6 and - 12 kcal/mol. We performed thermal shift assays on these molecules to examine their potential effects on the stability of HIV-1 capsid hexamer and found that two compounds, ZINC520357473 and ZINC4119064 increased the melting point of the latter by 14.8 °C and 33 °C, respectively. These results support the conclusion that the two ZINC compounds are primary hits targeting the capsid dimer interface. Our simulations also suggest that the two hit molecules may bind at the capsid dimer interface by occupying a new sub-pocket that has not been exploited by existing CA inhibitors. The possible causes for why other top-scored compounds suggested by ABFE filters failed to show measurable activity are discussed.

Crystal structures of an archaeal thymidylate kinase from Sulfolobus tokodaii provide insights into the role of a conserved active site Arginine residue.

Thymidylate kinase (TMK) is a key enzyme that plays an important role in DNA synthesis. Therefore, it serves as an attractive therapeutic target for the development of antibacterial, antiparasitic and anticancer drugs. Herein, we report the biochemical characterization and crystal structure determination of thymidylate kinase from a hyperthermophilic organism Sulfolobus tokodaii (StTMK) in its apo and ADP-bound forms. Our study describes the first three-dimensional structure of an archaeal TMK. StTMK is a thermostable enzyme with optimum activity at 80°C. Despite the overall similarity to homologous TMKs, StTMK structures revealed several residue substitutions at the active site. However, enzyme assays demonstrated specificity to its natural substrates ATP and dTMP. Analysis of the structures also revealed multiple conformational states of Arg93 which is located at the reaction centre and is a part of the highly conserved DRX motif. Only one of these states was found to be suitable for the proper positioning of the α-phosphate group of dTMP at the active site. Computational alanine scanning and MM/PBSA binding energy calculation revealed the importance of Arg93 side chain in substrate binding. Subsequent site directed mutagenesis at this position to an Ala resulted in the loss of activity. Thus, the computational and biochemical studies reveal the importance of Arg93 for enzyme function, while the different conformational states of Arg93 observed in the structural studies imply its regulatory role in the catalytically competent placement of dTMP.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Molecular dynamics study of HIV-1 RT-DNA-nevirapine complexes explains NNRTI inhibition and resistance by connection mutations.

HIV-1 reverse transcriptase (RT) is a multifunctional enzyme that is targeted by nucleoside analogs (NRTIs) and non-nucleoside RT inhibitors (NNRTIs). NNRTIs are allosteric inhibitors of RT, and constitute an integral part of several highly active antiretroviral therapy regimens. Under selective pressure, HIV-1 acquires resistance against NNRTIs primarily by selecting mutations around the NNRTI pocket. Complete RT sequencing of clinical isolates revealed that spatially distal mutations arising in connection and the RNase H domain also confer NNRTI resistance and contribute to NRTI resistance. However, the precise structural mechanism by which the connection domain mutations confer NNRTI resistance is poorly understood. We performed 50-ns molecular dynamics (MD) simulations, followed by essential dynamics, free-energy landscape analyses, and network analyses of RT-DNA, RT-DNA-nevirapine (NVP), and N348I/T369I mutant RT-DNA-NVP complexes. MD simulation studies revealed altered global motions and restricted conformational landscape of RT upon NVP binding. Analysis of protein structure network parameters demonstrated a dissortative hub pattern in the RT-DNA complex and an assortative hub pattern in the RT-DNA-NVP complex suggesting enhanced rigidity of RT upon NVP binding. The connection subdomain mutations N348I/T369I did not induce any significant structural change; rather, these mutations modulate the conformational dynamics and alter the long-range allosteric communication network between the connection subdomain and NNRTI pocket. Insights from the present study provide a structural basis for the biochemical and clinical findings on drug resistance caused by the connection and RNase H mutations.

Phenyl substituted 3-hydroxypyridin-2(1H)-ones: inhibitors of influenza A endonuclease.

Inhibition of the endonuclease activity of influenza RNA-dependent RNA polymerase is recognized as an attractive target for the development of new agents for the treatment of influenza infection. Our earlier study employing small molecule fragment screening using a high-resolution crystal form of pandemic 2009 H1N1 influenza A endonuclease domain (PAN) resulted in the identification of 5-chloro-3-hydroxypyridin-2(1H)-one as a bimetal chelating ligand at the active site of the enzyme. In the present study, several phenyl substituted 3-hydroxypyridin-2(1H)-one compounds were synthesized and evaluated for their ability to inhibit the endonuclease activity as measured by a high-throughput fluorescence assay. Two of the more potent compounds in this series, 16 and 18, had IC50 values of 11 and 23nM in the enzymatic assay, respectively. Crystal structures revealed that these compounds had distinct binding modes that chelate the two active site metal ions (M1 and M2) using only two chelating groups. The SAR and the binding mode of these 3-hydroxypyridin-2-ones provide a basis for developing a new class of anti-influenza drugs.

Discovery of safe and orally effective 4-aminoquinaldine analogues as apoptotic inducers with activity against experimental visceral leishmaniasis.

Novel antileishmanials are urgently required to overcome emergence of drug resistance, cytotoxic effects, and difficulties in oral delivery. Toward this, we investigated a series of novel 4-aminoquinaldine derivatives, a new class of molecules, as potential antileishmanials. 4-Aminoquinaldine derivatives presented inhibitory effects on L. donovani promastigotes and amastigotes (50% inhibitory concentration range, 0.94 to 127 µM). Of these, PP-9 and PP-10 were the most effective in vitro and demonstrated strong efficacies in vivo through the intraperitoneal route. They were also found to be effective against both sodium antimony gluconate-sensitive and -resistant Leishmania donovani strains in BALB/c mice when treated orally, resulting in more than 95% protection. Investigation of their mode of action revealed that killing by PP-10 involved moderate inhibition of dihydrofolate reductase and elicitation of the apoptotic cascade. Our studies implicate that PP-10 augments reactive oxygen species generation, evidenced from decreased glutathione levels and increased lipid peroxidation. Subsequent disruption of Leishmania promastigote mitochondrial membrane potential and activation of cytosolic proteases initiated the apoptotic pathway, resulting in DNA fragmentation and parasite death. Our results demonstrate that PP-9 and PP-10 are promising lead compounds with the potential for treating visceral leishmaniasis (VL) through the oral route.

Modeling the closed and open state conformations of the GABA(A) ion channel--plausible structural insights for channel gating.

Recent disclosure of high resolution crystal structures of Gloeobacter violaceus (GLIC) in open state and Erwinia chrysanthemii (ELIC) in closed state provides newer avenues to advance our knowledge and understanding of the physiologically and pharmacologically important ionotropic GABA(A) ion channel. The present modeling study envisions understanding the complex molecular transitions involved in ionic conductance, which were not evident in earlier disclosed homology models. In particular, emphasis was put on understanding the structural basis of gating, gating transition from the closed to the open state on an atomic scale. Homology modeling of two different physiological states of GABA(A) was carried out using their respective templates. The ability of induced fit docking in breaking the critical inter residue salt bridge (Glu155ß(2) and Arg207ß(2)) upon endogenous GABA docking reflects the perceived side chain rearrangements that occur at the orthosteric site and consolidate the quality of the model. Biophysical calculations like electrostatic mapping, pore radius calculation, ion solvation profile, and normal-mode analysis (NMA) were undertaken to address pertinent questions like the following: How the change in state of the ion channel alters the electrostatic environment across the lumen; How accessible is the Cl(-) ion in the open state and closed state; What structural changes regulate channel gating. A "Twist to Turn" global motion evinced at the quaternary level accompanied by tilting and rotation of the M2 helices along the membrane normal rationalizes the structural transition involved in gating. This perceived global motion hints toward a conserved gating mechanism among pLGIC. To paraphrase, this modeling study proves to be a reliable framework for understanding the structure function relationship of the hitherto unresolved GABA(A) ion channel. The modeled structures presented herein not only reveal the structurally distinct conformational states of the GABA(A) ion channel but also explain the biophysical difference between the respective states.

Enhancing preclinical drug discovery with artificial intelligence.

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

Poziotinib for EGFR exon 20-mutant NSCLC: Clinical efficacy, resistance mechanisms, and impact of insertion location on drug sensitivity.

We report a phase II study of 50 advanced non-small cell lung cancer (NSCLC) patients with point mutations or insertions in EGFR exon 20 treated with poziotinib (NCT03066206). The study achieved its primary endpoint, with confirmed objective response rates (ORRs) of 32% and 31% by investigator and blinded independent review, respectively, with a median progression-free survival of 5.5 months. Using preclinical studies, in silico modeling, and molecular dynamics simulations, we found that poziotinib sensitivity was highly dependent on the insertion location, with near-loop insertions (amino acids A767 to P772) being more sensitive than far-loop insertions, an observation confirmed clinically with ORRs of 46% and 0% observed in near versus far-loop, respectively (p = 0.0015). Putative mechanisms of acquired resistance included EGFR T790M, MET amplifications, and epithelial-to-mesenchymal transition (EMT). Our data demonstrate that poziotinib is active in EGFR exon 20-mutant NSCLC, although this activity is influenced by insertion location.

Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies.

The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables (X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

Combinatorial library enumeration and lead hopping using comparative interaction fingerprint analysis and classical 2D QSAR methods for seeking novel GABA(A) alpha(3) modulators.

Selective modulators of GABA(A) alpha(3) (gamma amino butyric acid alpha(3)) receptor are known to alleviate the side effects associated with nonspecific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using the combichem approach endorsed mining in a lead-like chemical space. Primary screening of the massive library was undertaken using the "Miscreen" toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset, thus, obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (comparative interaction fingerprint analysis). CoIFA encodes protein-ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABA(A) alpha(3) receptor, standard comparative modeling techniques were employed to construct a homology model of GABA(A) alpha(3) receptor. A typical docking study was then carried out on the modeled structure, and the interaction fingerprints generated based on the docked binding mode were used to derive propensity values for the interacting atom pairs that served as pseudo-energy variables to generate a CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus two-dimensional QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed using the concept of bioisosterism were found to be promising and could be considered for synthesis and subsequent screening.

Pan-Cancer Landscape and Analysis of ERBB2 Mutations Identifies Poziotinib as a Clinically Active Inhibitor and Enhancer of T-DM1 Activity.

We characterized the landscape and drug sensitivity of ERBB2 (HER2) mutations in cancers. In 11 datasets (n = 211,726), ERBB2 mutational hotspots varied across 25 tumor types. Common HER2 mutants yielded differential sensitivities to eleven EGFR/HER2 tyrosine kinase inhibitors (TKIs) in vitro, and molecular dynamics simulations revealed that mutants with a reduced drug-binding pocket volume were associated with decreased affinity for larger TKIs. Overall, poziotinib was the most potent HER2 mutant-selective TKI tested. Phase II clinical testing in ERBB2 exon 20-mutant non-small cell lung cancer resulted in a confirmed objective response rate of 42% in the first 12 evaluable patients. In pre-clinical models, poziotinib upregulated HER2 cell-surface expression and potentiated the activity of T-DM1, resulting in complete tumor regression with combination treatment.

Spices: Potential Therapeutics for Alzheimer's Disease.

India has traditionally been known to all over the world for spices and medicinal plants. Spices exhibit a wide range of pharmacological activities. In contemporary, Indian spices are used to rustle up delicious delicacies. However, the Indian spices are more than just adjuvant which adds aroma and fragrance to foods. A few spices are very widely used and grown commercially in many countries, contain many important chemical constituents in the form of essential oil, oleoresin, oleogum, and resins, which impart flavor, pungency, and color to the prepared dishes, simultaneously exerts diverse therapeutic benefits. Ayurveda, the traditional systems of medicine in India has many evidences for the utilization of spices to cure various diseases. Some of the activities have been scientifically proven. Among various indications central nervous system disorders are of prime importance and it has been evident in traditional books and published reports that spices in fact protect and cure neuronal ailments. Likewise there are many spices found in India used for culinary purpose and have been found to have reported specific activities against brain disorders. About 400 B.C., Hippocrates rightly said "Let food be thy medicine and medicine thy food." This review focuses on the importance of spices in therapeutics and the till date scientific findings of Indian spices in CNS pharmacology and explores the potential of Indian spices to cure CNS disorders.

Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation.

Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.

Structural propensities of kinase family proteins from a Potts model of residue co-variation.

Understanding the conformational propensities of proteins is key to solving many problems in structural biology and biophysics. The co-variation of pairs of mutations contained in multiple sequence alignments of protein families can be used to build a Potts Hamiltonian model of the sequence patterns which accurately predicts structural contacts. This observation paves the way to develop deeper connections between evolutionary fitness landscapes of entire protein families and the corresponding free energy landscapes which determine the conformational propensities of individual proteins. Using statistical energies determined from the Potts model and an alignment of 2896 PDB structures, we predict the propensity for particular kinase family proteins to assume a "DFG-out" conformation implicated in the susceptibility of some kinases to type-II inhibitors, and validate the predictions by comparison with the observed structural propensities of the corresponding proteins and experimental binding affinity data. We decompose the statistical energies to investigate which interactions contribute the most to the conformational preference for particular sequences and the corresponding proteins. We find that interactions involving the activation loop and the C-helix and HRD motif are primarily responsible for stabilizing the DFG-in state. This work illustrates how structural free energy landscapes and fitness landscapes of proteins can be used in an integrated way, and in the context of kinase family proteins, can potentially impact therapeutic design strategies.

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand-receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein-ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.

Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors.

Structural coverage of the human kinome has been steadily increasing over time. The structures provide valuable insights into the molecular basis of kinase function and also provide a foundation for understanding the mechanisms of kinase inhibitors. There are a large number of kinase structures in the PDB for which the Asp and Phe of the DFG motif on the activation loop swap positions, resulting in the formation of a new allosteric pocket. We refer to these structures as "classical DFG-out" conformations in order to distinguish them from conformations that have also been referred to as DFG-out in the literature but that do not have a fully formed allosteric pocket. We have completed a structural analysis of almost 200 small molecule inhibitors bound to classical DFG-out conformations; we find that they are recognized by both type I and type II inhibitors. In contrast, we find that nonclassical DFG-out conformations strongly select against type II inhibitors because these structures have not formed a large enough allosteric pocket to accommodate this type of binding mode. In the course of this study we discovered that the number of structurally validated type II inhibitors that can be found in the PDB and that are also represented in publicly available biochemical profiling studies of kinase inhibitors is very small. We have obtained new profiling results for several additional structurally validated type II inhibitors identified through our conformational analysis. Although the available profiling data for type II inhibitors is still much smaller than for type I inhibitors, a comparison of the two data sets supports the conclusion that type II inhibitors are more selective than type I. We comment on the possible contribution of the DFG-in to DFG-out conformational reorganization to the selectivity.

3-Hydroxyquinolin-2(1H)-ones As Inhibitors of Influenza A Endonuclease.

Several 3-hydroxyquinolin-2(1H)-ones derivatives were synthesized and evaluated as inhibitors of 2009 pandemic H1N1 influenza A endonuclease. All five of the monobrominated 3-hydroxyquinolin(1H)-2-ones derivatives were synthesized. Suzuki-coupling of p-fluorophenylboronic acid with each of these brominated derivatives provided the respective p-fluorophenyl 3-hydroxyquinolin(1H)-2-ones. In addition to 3-hydroxyquinolin-2(1H)-one, its 4-methyl, 4-phenyl, 4-methyl-7-(p-fluorophenyl), and 4-phenyl-7-(p-fluorophenyl) derivatives were also synthesized. Comparative studies on their relative activity revealed that both 6- and 7-(p-fluorophenyl)-3-hydroxyquinolin-2(1H)-one are among the more potent inhibitors of H1N1 influenza A endonuclease. An X-ray crystal structure of 7-(p-fluorophenyl)-3-hydroxyquinolin-2(1H)-one complexed to the influenza endonuclease revealed that this molecule chelates to two metal ions at the active site of the enzyme.