Type 2 diabetes (T2D) is a metabolic syndrome frequently associated with obesity and endoplasmic reticulum stress-mediated inflammation, which can trigger the unfolded protein response (UPR), impair insulin signaling, and promote apoptosis. To identify potential natural therapeutic candidates, this study investigated the mechanisms of action of 14 compounds from Uncaria tomentosa (UT), a medicinal plant from the Amazon rainforest, using in silico modeling. The study focused on the UPR, TRAF2/JNK pro-inflammatory signaling pathway, and insulin signaling pathways, which play key roles in T2D. Some of the UT compounds were docked against several human proteins involved in these pathways, and molecular dynamics simulations confirmed stable interactions between the target proteins (PERK, TRAF2, JNK, TNF-α, IRS-1, PI3K, AKT, GSK3β, and PPARγ) and four of the UT compounds, 5-Carboxystrictosidine, Cinchonain, Epicatechin and Mitraphylline. Additionally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties analyses were conducted to predict the four compounds, revealing suitable pharmacokinetic properties. These findings suggest that specific UT compounds may be used in experimental tests to whether investigate their therapeutic potential in managing T2D by modulating signaling pathways related to the conditions UPR, inflammation, and insulin resistance.

Abstract Introduction: mTORC1 activity is widely regarded as oncogenic; however, under therapeutic stress, suppression of mTORC1 can promote adaptive survival programs and contribute to drug resistance. Despite extensive efforts to inhibit mTORC1, pharmacologic strategies to transiently activate this pathway remain unexplored. Physiologic activation of mTORC1 occurs through inhibition of the tuberous sclerosis complex (TSC), suggesting TSC2 as a druggable node to therapeutically rewire stress responses in cancer. Moreover, p53-mutant acute myeloid leukemia (AML) represents a high-risk disease state characterized by profound resistance to cytotoxic therapy. Methods: Through structure-guided in silico screening and medicinal chemistry optimization, we developed AcTor, a first-in-class small-molecule inhibitor of TSC2. Based on the established vulnerability of TSC2-deficient cells to proteasome inhibition, we evaluated AcTor in combination with the proteasome inhibitor ixazomib (IXZ) in AML cell lines, patient-derived samples, and xenograft models. Results: AcTor markedly potentiated IXZ-induced cytotoxicity across genetically diverse AML models. The combination triggered rapid apoptosis driven by mitochondrial dysfunction, characterized by loss of mitochondrial integrity and bioenergetic failure. Transcriptomic profiling revealed induction of a p53-associated stress response in p53-deficient AML cells treated with AcTor/IXZ, indicating activation of non-canonical p53 pathway outputs independent of p53 genotype. In vivo, brief exposure to AcTor combined with IXZ significantly suppressed leukemic burden in patient-derived AML xenografts, irrespective of p53 status. This response was associated with efficient elimination of circulating blasts and leukemic stem cells. Notably, sensitivity to the combination was preserved in relapsed disease models. Conclusions: These findings identify TSC2 inhibition as a previously unrecognized therapeutic strategy to activate mTORC1 in a controlled, context-dependent manner. While AcTor lacks antileukemic activity as a single agent, its combination with proteasome inhibition converts mTORC1 signaling from a survival pathway into a driver of mitochondrial catastrophe. This mechanistically defined vulnerability enables durable targeting of aggressive and treatment-refractory AML and provides a strong rationale for translational development of mTORC1-activating combination therapies. Citation Format: Shakti Pattanayak, Boaz Tirosh, Omid Hajihassani, Jordan M. Winter, Kelsey H Fisher-Wellman, Leif A Eriksson. Pharmacologic TSC2 inhibition sensitizes acute myeloid leukemia to proteasome inhibition via mTORC1 mediated mitochondrial catastrophe [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB354.

BACKGROUND:mTORC1 activity is oncogenic. However, in the presence of chemotherapy, suppression of mTORC1 is cytoprotective. mTOR suppression requires an intact tuberous sclerosis complex (TSC), composed of TSC1, TSC2 and TBC1D7. Small molecules that activate mTOR by blocking the TSC are lacking. METHODS:We applied in silico docking and medicinal chemistry to generate AcTor, a potential first-of-its-kind TSC2 inhibitor. Because inhibition of TSC2 results in increased sensitivity to proteasome inhibitors, we combined AcTor and the proteasome inhibitor ixazomib (IXZ) in various cancer cell types. RESULTS:Potentiation of cytotoxic activity of IXZ by AcTor was observed across multiple acute myeloid leukemia (AML) cell lines and primary patient samples. The combination triggered a collapse of mitochondrial respiratory capacity, loss of mitochondrial membrane potential, accumulation of ROS and apoptosis. These attributes increased in drug-resistant AML. Transcriptomic profiling revealed that AcTor alone induced anabolic and oxidative phosphorylation programs, whereas AcTor/IXZ redirected the signaling towards stress-associated and pro-apoptotic transcriptional states, including a p53 pathway signature. In vivo studies revealed reduction in AML burden, depletion of blasts and of leukemic stem cells, and retention of activity upon relapse. AcTor/IXZ was equally potent in a TP53-mutated patient-derived xenograft model, exceeding the efficacy of standard-of-care. CONCLUSIONS:As a TSC2 inhibitor, AcTor should not be used alone in cancer. When combined with proteasome inhibitors, the pharmacodynamics of AcTor shifts towards the development of a mitochondrial catastrophe in AML, which is durable, broad range, agnostic to TP53 mutations and to the acquisition of resistance to common clinical anti-AML drugs.

IntroductionGraphene oxide (GO) nanosheets have attracted significant interest as potential carriers for drug delivery due to their unique physicochemical properties and large surface area. However, concerns regarding their cytotoxicity and biodegradability must be addressed before clinical translation. In this study, we aimed to evaluate the biocompatibility and biodegradation of GO functionalized with polyethylene glycol (PEG) and polyethyleneimine (PEI), two commonly used polymers in biomedical applications.MethodThe interactions of GO, GO-PEG, and GO-PEI with granulocyte-like cells were investigated to determine their effects on cell viability and their susceptibility to immune-mediated degradation. Biodegradation of the materials was assessed using Raman spectroscopy after exposure to granulocyte-like cells, neutrophil extracellular traps (NETs), and myeloperoxidase (MPO), a key enzyme present in NETs. In addition, circular dichroism (CD) spectroscopy was used to evaluate structural changes in MPO upon interaction with the materials, and molecular dynamics simulations were performed to investigate the interaction of hypochlorous acid (HOCl), the catalytic product of MPO, with GO and its functionalized derivatives.Results and discussionThe results showed that functionalization with PEG or PEI significantly improved cell viability compared with pristine GO. Although GO was structurally modified by granulocyte-like cells, NETs, and MPO, GO-PEG did not show significant degradation under these conditions. In contrast, GO-PEI was susceptible to structural modification by both NETs and MPO. CD analysis indicated that MPO maintained a more stable secondary structure in the presence of GO-PEI compared with GO or GO-PEG under oxidative conditions, suggesting that MPO-generated HOCl may play a key role in GO-PEI degradation. Molecular dynamics simulations further demonstrated stronger interaction and retention of HOCl in GO-PEG and GO-PEI systems compared with pristine GO, indicating an enhanced interaction between oxidants and the functionalized materials. Overall, these findings demonstrate that polymer functionalization significantly influences the biocompatibility and immune-mediated structural modification of GO. Importantly, this study provides new mechanistic insights into how PEG and PEI modifications affect MPO-driven oxidative degradation pathways of graphene oxide-based nanomaterials. These results highlight GO-PEI as a biodegradable and biocompatible candidate for future biomedical and drug delivery applications.

PROteolysis TArgeting Chimeras (PROTACs) are bifunctional molecules designed to induce targeted protein degradation by forming a transient ternary complex between an E3 ubiquitin ligase and a protein of interest (POI), leading to the E3-mediated ubiquitination of the POI and its subsequent proteasomal degradation. Although PROTACs have emerged as highly promising therapeutic tools, rational design remains challenging due to limited structural understanding of the resulting assemblies, the dynamic nature of the ternary interface, and the critical role of the linker. Herein, we present COMPASS (COmputational Modeling of PROTAC Assembly with Structure-based Screening), a computational pipeline that allows the screening of linker libraries by assessing both ternary complex formation and ubiquitination potential. COMPASS functions as a high-sensitivity negative filter, identifying linkers that cannot form productive complexes and enabling their elimination before synthesis. Benchmarking against 20 crystallographic structures yielded <6 Å Cα-RMSD across all systems, outperforming existing methods. Retrospective validation across 8 distinct E3/POI systems (112 PROTACs) yielded 93% recall against degradation endpoints. Discriminative power is strongest when linker geometry is rate-limiting, a regime complementary to the stability and cooperativity effects that static structural modeling cannot capture.

Computer simulations as learning tools in chemistry enable student centered pedagogies, with requirements for adequate instructional support. The provision of support to scaffold and increase learning can be accomplished through assignments that instruct and challenge students toward productive uses of a simulation. In this design research study, assignments for simulation exercises in chemistry were developed in collaboration with teachers and used in a classroom experiment with a switching replications design. Multiple choice tests and classroom observations were complemented by student interviews about their learning experience. Statistical analyses reveal similar learning gains in the treatment and control groups. Results from interviews show that students value the opportunity to explore simulations by themselves but also emphasize the importance of prior and concurrent explanations. Students acknowledge the roles of questions, instructions, tables, and other prompts in learning from the assignment. They also express that productive interaction with simulations to extract relevant information was promoted by drawings and verbal expressions, whereby students discerned mechanisms and conditions of phenomena modeled in the simulations. The findings suggest rationales for strategic combinations of elements from both conditions and features to consider in the design and adaptation of assignment manuals for simulation exercises.

This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training data set of 221 million unique compounds, MolAI employs an autoencoder neural machine translation model to generate latent space representations of molecules. The model demonstrated exceptional performance through extensive validation, achieving an accuracy of >99.8% in regenerating input molecules from their corresponding latent space. This study showcases the effectiveness of MolAI-driven molecular descriptors by developing an ML-based model (iLP) that accurately predicts the predominant protonation state of molecules at neutral pH. These descriptors also significantly enhance ligand-based virtual screening and are successfully applied in a framework (iADMET) for predicting ADMET features with high accuracy. This capability of encoding and decoding molecules to and from latent space opens unique opportunities in drug discovery, structure-activity relationship analysis, hit optimization, de novo molecular generation, and training infinite machine learning models.

In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable speed and precision. Uniquely, iScore circumvents the conventional reliance on explicit knowledge of protein-ligand interactions and a full picture of atomic contacts, instead leveraging a set of ligand and binding pocket descriptors to directly evaluate binding affinity. This approach enables skipping the inefficient and slow conformational sampling stage, thereby enabling the rapid screening of ultrahuge molecular libraries, a crucial advancement given the practically infinite dimensions of chemical space. iScore was rigorously trained and validated using the PDBbind 2020 refined set, CASF 2016, CSAR NRC-HiQ Set1/2, DUD-E, and target fishing data sets, employing three distinct ML methodologies: Deep neural network (iScore-DNN), random forest (iScore-RF), and eXtreme gradient boosting (iScore-XGB). A hybrid model, iScore-Hybrid, was subsequently developed to incorporate the strengths of these individual base learners. The hybrid model demonstrated a Pearson correlation coefficient (R) of 0.78 and a root-mean-square error (RMSE) of 1.23 in cross-validation, outperforming the individual base learners and establishing new benchmarks for scoring power (R = 0.814, RMSE = 1.34), ranking power (ρ = 0.705), and screening power (success rate at top 10% = 73.7%). Moreover, iScore-Hybrid demonstrated great performance in the target fishing benchmarking study.

The apoptosome, a critical protein complex in apoptosis regulation, relies on intricate interactions between its components, particularly the proteins containing the Caspase Activation and Recruitment Domain (CARD). This work presents a thorough computational analysis of the stability and specificity of CARD-CARD interactions within the apoptosome. Departing from available crystal structures, we identify important residues for the interaction between the CARD domains of Apaf-1 and Caspase-9. Our results underscore the essential role of these residues in apoptosome activity, offering prospects for targeted intervention strategies. Available experimental complex structures were able to validate the protein-protein docking consensus approach used herein. We furthermore extended our analysis to explore the specificity of CARD-CARD interactions by cross-docking experiments between apoptosome and PIDDosome components, between which there should not be any interaction despite belonging to the same death fold subfamily. Our findings indicate that native interactions within individual complexes exhibit greater stability than the cross-docked complexes, emphasizing the specificity required for effective protein complex formation. This study enhances our understanding of apoptotic regulation and demonstrates the utility of computational approaches in elucidating intricate protein-protein interactions.

Proteasome inhibitors (PIs) constitute the first line of therapy for multiple myeloma (MM). Despite the impressive clinical efficacy, MM remains fatal due to the development of drug resistance over time. During MM progression, stress responses to hypoxia and PIs suppress mammalian target of rapamycin complex 1 (mTORC1) activity by releasing tuberous sclerosis complex 2 (TSC2), which deactivates Ras homologue enriched in brain (Rheb), a crucial regulator of mTORC1. The efficacy of PIs targeting MM is enhanced when mTORC1 is hyperactivated. We thus propose that the inhibition of TSC2 will improve the efficacy of PIs targeting MM. To the best of our knowledge, no cocrystallized structure of the TSC2-Rheb complex has been reported. We therefore developed a representative model using the individual structures of TSC2 (PDB: 7DL2) and Rheb (PDB: 1XTS). Computational modeling involving an extensive protein-protein docking consensus approach was performed to determine the putative binding mode of TSC2-Rheb. The proposed docking poses were refined, clustered, and evaluated by MD simulations to explore the conformational dynamics and protein mobility, particularly at the drug-binding interface of TSC2-Rheb. Our results agree with the suggested binding mode of TSC2-Rheb previously reported in the literature. The results reported herein establish a basis for the development of new inhibitors blocking the binding of TSC2 and Rheb, aiming to reinstate mTORC1 activation and facilitate improved efficacy of PIs against multiple myeloma.

The multifactorial nature of cancer requires treatment that involves simultaneous targeting of associated overexpressed proteins and cell signaling pathways, possibly leading to synergistic effects. Herein, we present a systematic study that involves the simultaneous inhibition of human topoisomerases (hTopos) and histone deacetylases (HDACs) by multitargeted quinoline-bridged hydroxamic acid derivatives. These compounds were rationally designed considering pharmacophoric features and catalytic sites of the cross-talk proteins, synthesized, and assessed for their anticancer potential. Our findings revealed that the compound 5c significantly produced anticancer effects in vitro and in vivo by reducing the tumor growth and its size in the A549 cell-induced lung cancer xenograft model through multiple mechanisms, primarily by multi-inhibition of hTopoI/II and HDACs, especially HDAC1 via atypical binding. The present paper discusses detailed mechanistic biological investigations, structure-activity effects supported by computational docking studies, and DMPK studies and provides future scope for lead optimization and modification.

Lung cancer is the leading cause of death by cancer in the world and finding new targets is a major medical need to tackle this disease. Here, upon proteomic analysis to identify common players in oncogenic EGFR- and KRAS-driven lung adenocarcinoma mouse models, we uncovered a largely unknown protein in cancer, flavin-containing monooxygenase 4 (FMO4), whose expression was increased in lung tumors compared with adjacent lung tissue. FMO4 expression was strongly increased also in lung cancer samples from patients compared with healthy lung, and its expression level was inversely correlated with overall survival. Remarkably, in vivo deletion of FMO4 greatly decreased tumor burden and increased survival in oncogenic KRAS-driven lung adenocarcinoma mice unveiling its crucial role in tumor biology. Mechanistically, we found that FMO4 loss of function promotes ferroptosis and cooperates with ferroptosis inducers in vitro and in vivo . Moreover, FMO4 facilitates the interaction between MAT2A and MAT2B, promoting the generation of cysteine from methionine, which in turn boosts the generation of glutathione, thus protecting lung adenocarcinoma against ferroptosis. In summary we identified a new target in lung adenocarcinoma with important implications in cancer biology. ### Competing Interest Statement The authors have declared no competing interest.

Inositol Requiring Enzyme 1 (IRE1) is a bifunctional serine/threonine kinase and endoribonuclease identified as therapeutic target in multiple diseases. Inspired by the recent work on the assessment of lysine and cysteine reactivities, we present a simple and intuitive protocol for the assessment of reactive lysine, while characterizing a unique protonation state of Lys599 located in the kinase domain. Using Quantum Mechanics/Molecular Mechanics (QM/MM) calculations, QM/MM well-tempered metadynamics simulations (QM/MM WT-MetaD), and classical Molecular Dynamics (MD), we have investigated inhibitor binding in three different states of the IRE1 kinase: (i) DFG-in/αC-in (DICI) conformation; (ii) the DFG-out/αC-out (DOCO) conformation, and (iii) the DFG-in/αC-out (DICO) conformation. Our findings reveal a unique proton transfer from the sidechain of the β3-strand Lys599 to Glu612 of the αC-helix. Our results allow for accurately defining the geometry of the hydrogen bonds occurring in the IRE1 kinase active state and distinguishing structurally closely related inactive states by analyzing the formation/disruption of crucial hydrogen bonds in the Lys599-Glu612-Asp711 triad. Our work prompts further studies in IRE1 and other kinases to characterize possibly conserved drug binding mechanisms that might lead to a novel structural paradigm in kinase drug discovery.

Industrialization, fast food intake and reduced physical activity, mainly in developed countries, exacerbate obesity and make it a major lifestyle disorder. A promising strategy for developing effective anti-obesity agents is to inhibit pancreatic lipase, thereby reducing lipid absorption. Currently, the only clinically approved pharmacological agent for pancreatic lipase inhibition is Orlistat. However, its undesirable gastrointestinal side effects have prompted the search for more effective and potent drugs. This study investigates the inhibitory mechanism of Bromhexine, a mucolytic drug, on pancreatic lipase using Lineweaver–Burk plot analysis and molecular docking, along with simulations, and compares its efficacy to that of the Food and Drug Administration (FDA) approved drug Orlistat. Kinetic analysis indicates that Bromhexine exhibits mixed inhibition of pancreatic lipase, with IC 50 and K i values of 360 µM and 450 µM, respectively, which are comparable to those of Orlistat. Molecular docking confirms that Bromhexine interacts with the His263 residue in the enzyme’s active site through hydrogen bonding, similar to Orlistat, thereby reducing the enzyme’s affinity for its natural substrate. Binding pose metadynamics (BPMD) simulations further supports the stability of Bromhexine’s interactions. Collectively, our findings suggest that Bromhexine displays potent pancreatic lipase (PL) inhibition activity and could serve as a potential candidate in weight management as demonstrated by both in silico and in vitro analyses. However, further investigations, including structure-activity relationship (SAR) analyses and in vivo studies, are necessary to confirm its clinical potential as a pancreatic lipase inhibitor.

Pathological amyloids associated with Parkinson and Alzheimer diseases have been shown to catalyze chemical reactions in vitro. To elucidate how small-molecule substrates interact with cross-β amyloid structures, we here employ computational approaches to investigate α-synuclein amyloid fibrils of the type-1A fold. Our initial binding pocket prediction analysis identified three distinct substrate-binding sites per protofilament, yielding a total of six sites in the dimeric type-1A amyloid structure. Molecular docking of the model phosphoester substrate para-nitrophenyl phosphate (pNPP), previously shown to be dephosphorylated by α-synuclein amyloids in vitro, was performed on the three identified sites. Docking was validated by molecular dynamics simulations for a period of 100 ns. The results revealed a pronounced preference for a single binding site (termed Site 2), as pNPP migrated to this region when primarily placed at the other two sites. Site 2 is located near the interface between the two protofilaments in a cavity enriched with lysine residues and histidine-50. Binding site analysis suggests stable, yet dynamic, interactions between pNPP and these residues in the α-synuclein amyloid fibril. Our work provides molecular-mechanistic details of the interaction between a small-molecule substrate and one α-synuclein amyloid polymorph. This framework may be extended to other reactive substrates and amyloid polymorphs.

Adaptation to the shortage in free amino acids (AA) is mediated by 2 pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the 2 pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.

The enzyme deoxyhypusine synthase (DHS) catalyzes the first step in the post-translational modification of the eukaryotic translation factor 5A (eIF5A). This is the only protein known to contain the amino acid hypusine, which results from this modification. Both eIF5A and DHS are essential for cell viability in eukaryotes, and inhibiting DHS is a promising strategy to develop new therapeutic alternatives. DHS proteins from many are sufficiently different from their human orthologs for selective targeting against infectious diseases; however, no DHS inhibitor selective for parasite orthologs has previously been reported. Here, we established a yeast surrogate genetics platform to identify inhibitors of DHS from Plasmodium vivax, one of the major causative agents of malaria. We constructed genetically modified Saccharomyces cerevisiae strains expressing DHS genes from Homo sapiens (HsDHS) or P. vivax (PvDHS) in place of the endogenous DHS gene from S. cerevisiae. Compared with a HsDHS complemented strain with a different genetic background that we previously generated, this new strain background was ~60-fold more sensitive to an inhibitor of human DHS. Initially, a virtual screen using the ChEMBL-NTD database was performed. Candidate ligands were tested in growth assays using the newly generated yeast strains expressing heterologous DHS genes. Among these, two showed promise by preferentially reducing the growth of the PvDHS-expressing strain. Further, in a robotized assay, we screened 400 compounds from the Pathogen Box library using the same S. cerevisiae strains, and one compound preferentially reduced the growth of the PvDHS-expressing yeast strain. Western blot revealed that these compounds significantly reduced eIF5A hypusination in yeast. The compounds showed antiplasmodial activity in the asexual erythrocyte stage; EC50 in high nM to low μM range, and low cytotoxicity. Our study demonstrates that this yeast-based platform is suitable for identifying and verifying candidate small molecule DHS inhibitors, selective for the parasite over the human ortholog.

Pseudomonas aeruginosa is an opportunistic pathogen prone to developing drug-resistance and is a major cause of infection for burn patients and patients suffering from cystic fibrosis or are hospitalized in intensive care units. One of the virulence factors of this bacterium is the lipase enzyme that degrades the extracellular matrix of the host tissue and promotes invasion. Bromhexine is a mucolytic drug and has recently been reported to function as a competitive inhibitor of lipase with an IC50 value of 49 µM. In the present study, an attempt was made to identify stronger inhibitors from the ChEMBL database of bioactive compounds, as compared to the reference compound Bromhexine. Following docking and MD simulations, four hit compounds (N1-N4) were selected that showed promising binding modes and low RMSD values indicative of stable protein-ligand complexes. From subsequent binding pose metadynamics (BPMD) simulations, two of these (N2 and N4) stood out as more potent than Bromhexine, displaying stable interactions with residues in the catalytic site of the enzyme. Biological investigations were performed for all four compounds. Among them, the same two hit compounds were found to be the most effective binders with IC50 values of 22.1 and 27.5 µM, respectively; i.e. roughly twice as efficient as the reference Bromhexine. Taken together, our results show that these hits can be promising new candidates to use as leads for the development of drugs targeting the P. aeruginosa lipase enzyme.Communicated by Ramaswamy H. Sarma.