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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Quantitative proteome analysis identifies MAP2K6 as potential regulator of LIFR-induced radioresistance in nasopharyngeal carcinoma cells

Using Tandem Mass Tags (TMT) labeling and LC-MS/MS analysis of peptides from two cell lines (CNE2 and its radioresistant subclone CNE2-IR), we identified 754 proteins differentially expressed in CNE2-IR compared to CNE2. MAP2K6 was identified as a candidate radioresistance-related protein kinase. In vitro functional analysis revealed that over-expression of MAP2K6 significantly enhanced cell survival and colony formation following irradiation in NPC cells. Further, knockdown of MAP2K6 in radioresistant NPC cells led to decreased colony formation and increased apoptotic cells following irradiation. However, the effect of MAP2K6 in regulating the radioresistance in NPC cells did not seem to depend on p38/MAPK activity. Importantly, MAP2K6 might be required for leukemia inhibitory factor receptor (LIFR)-regulated radioresistance, as the expression levels of MAP2K6 affected LIFR/p70S6K signaling activation in NPC cells. Further, MAP2K6 kinase activity is required to activate LIFR/p70S6K signaling and to confer pro-survival effect on NPC cells. In conclusion, MAP2K6 might be an important regulator of LIFR-induced radioresistance in NPC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Gene expression profiling in adipose tissue of Sprague Dawley rats identifies olfactory receptor 984 as a potential obesity treatment target

Highlights: • Olr984 is identified as candidate gene for obesity resistance in rats. • Olr984 is highly expressed in human adipose tissue. • Olr984mRNA expression in human adipose tissue correlates with obesity parameters. The aim of the study was to identify and functionally characterize novel candidate gene/s involved in the development of resistance to diet-induced obesity in rats. In a high-fat-diet (HFD) study of rats, we found subgroups which either developed resistance to HFD-induced obesity (DR) or showed an obesity-prone phenotype (DIO). Gene expression analysis in 10 samples (5 DIO vs 5 DR) was performed. The most promising gene, OR6C3 (orthologous with rat Olr984 and mouse Olfr788) was measured by qRT-PCR in paired samples of human visceral (Vis) and subcutaneous (SC) adipose tissue (AT) (n = 225) and in sub-fractions of adipocytes and cells of stromal vascular fraction. Gene expression analyses showed Olr984 with significantly reduced mRNA expression in DR rats. In the Vis AT of human samples we found an up-regulation of OR6C3 compared to SC AT, independent of gender, glucose tolerance or type 2 diabetes. We observed significantly lower levels of SC AT OR6C3 mRNA in subjects with obesity compared to those with normal-weight or overweight. OR6C3 is more expressed in SVF than in adipocytes. Olr984 could be a novel candidate gene related to diet-induced obesity in rats. Variation in human AT mRNA expression is related to obesity parameters and glucose homeostasis and linked to the regulatory role of insulin on the Olr984.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗

DOE OSTI2018

A framework for monitoring-based commissioning: Identifying variables that act as barriers and enablers to the process

The practice of monitoring-based commissioning (MBCx) using energy management and information systems (EMIS) has been shown to enable and help sustain up to 20% energy savings in buildings. Despite research that has quantified the costs, benefits, and energy savings of MBCx, the process remains under-utilized. To understand why MBCx is not more frequently adopted and how to encourage its use, this research synthesizes qualitative data from over 40 organizations, currently engaging in MBCx. The outcome of this research is a framework containing variables that emerged from the qualitative data, marked as barriers or enablers, organized by phases of the MBCx process. The framework is comprised of 51 emergent variables that fall within 13 different categories. The variables that most frequently act as barriers are data configuration, measurement & verification (M&V), developing specifications for EMIS, and data architecture. Although some variables that act as barriers for one organization were identified as enablers for another. For example, payback/ROI was considered a barrier 7 times and an enabler 3 times. One organization had difficulty making the business case for the initial investment for MBCx due to lack of cost information, while another was able to justify large investments with documented savings of previously implemented measures identified through MBCx. The framework formally validates barriers found in previous research, and can be used by practitioners to better understand common experiences with MBCx. This research also highlights the need for a similar collective data set to validate common enablers to MBCx and also the need for empirical research to determine relationships between variables.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Identifying Disadvantaged Communities in the United States: An Energy-Oriented Mapping Tool that Aggregates Environmental and Socioeconomic Burdens

This paper defines a policy-relevant nationwide composite index to identify communities disproportionately impacted by environmental, energy, and climate injustices in the United States. We review existing vulnerability indicators and indices to assess the tradeoffs of different design parameters, including variable selection, geographic unit, dimensionality reduction, weighting, and aggregation methods. From this methodological review, we create the first nationwide, census tract-level index of cumulative burden that includes energy-relevant indicators alongside climate, social, environmental, and economic indicators, and is flexible to the inclusion of additional data sources. We provide a summary of the sources of inputs used to develop a definition for "disadvantaged communities" that can be used to prioritize energy investments. We discuss use-cases for this index including the implementation of the Justice40 Initiative, which calls for 40% of certain federal clean energy benefits to flow to disadvantaged communities in the United States. We use our results to examine historic allocations of federal energy investments and show that communities that we identify as disadvantaged received about 37% fewer funds per capita than non-disadvantaged communities.

cumulative burden↗

DOE OSTI2019

A method to identify weak points of interconnection of renewable energy resources

The increasing penetration of renewable energy resources (RERs) is challenging power grid planning and operation in maintaining grid reliability. Potential power grid reliability and stability issues may arise when large-scale RERs are connected to a weak power grid. These issues may become more significant due to the mutual interactions among RERs that are interconnected electrically close through a power network. Weakness analysis based on grid strength assessment is useful for identifying the potential weak grid issues. However, when taking into account the impact of the interactions among RERs, the weakness analysis becomes computationally challenging. Different combinations of points of interconnections (POIs) of RERs may have different impacts on grid strength at each POI. Due to the combination nature, such weakness analysis may be time-consuming when identifying the weakest combination of POIs from a large number of potential candidates in realistic power grids. To address this concern, the impact of the power network structure on grid strength is analyzed in this paper. It is found that the impact mainly depends on the impedance ratios at POIs. The findings can be summarized as two criteria to develop a recursive method for fast identification of the weakest combination in this paper. The efficacy of the proposed method is demonstrated through case studies on the IEEE 39-bus system and a realistic power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Identifying high-impact and high-uncertainty parameters in MiniFuel model predictions

The MiniFuel irradiation platform at Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR) is a flexible, high-throughput separate effects test capability. Finite element thermal models are relied upon to design MiniFuel experiments and to achieve experimental objectives. Recent reports show good agreement in the model prediction of target fuel temperatures, but as the capability of the experiments is extended to higher temperatures, the uncertainty in the model predictions must be quantified. To that end, high-impact, high-uncertainty parameters that contribute the most uncertainty to the model are identified. The uncertainty quantification was accomplished through a series of screening and sensitivity analyses. The first analysis utilizes the method of Morris to perform a computationally efficient preliminary screening that considers uncertainty in a large number of the model inputs. The most important parameters identified in the Morris screening study were then considered in a Sobol sensitivity analysis that more robustly ranks and quantifies the uncertainty associated with each parameter. From these analyses, it was determined that thermal contact conductance between components is the parameter that contributes the highest uncertainty. The estimated uncertainty of the MiniFuel model fuel temperature predictions is ±80 °C in the removable beryllium and ±40 °C in the vertical experiment facilities. In conclusion, the framework established by the series of sensitivity analyses presented herein could easily be adapted to fit the needs of accelerated fuel qualification processes.

Fuel, Irradiation↗

DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a pulmonary pathogen of major global concern. A key feature of Mtb infection in primates is the formation of granulomas, dense cellular structures surrounding infected lung tissue. These structures serve as the main site of host–pathogen interaction in TB, and thus to effectively treat TB we must clarify mechanisms of granuloma formation and their function in disease. Fibrotic granulomas are associated with both good and bad dis- ease outcomes. Fibrosis can serve to isolate infected tissue from healthy tissue, but it can also cause difficulty breathing as it leaves scars. Little is known about fibrosis in TB, and data from non-human primates is just beginning to clarify the picture. This work focuses on constructing a hybrid multi-scale model of fibrotic granuloma formation, in order to identify mechanisms driving development of fibrosis in Mtb infected lungs. We combine dynamics of molecular, cellular, and tissue scale models from previously published studies to characterize the formation of two common sub-types of fibrotic granulomas: peripherally fibrotic, with a cuff of collagen surrounding granulomas, and centrally fibrotic, with collagen throughout granulomas. Uncertainty and sensitivity analysis, along with large simulation sets, enable us to identify mechanisms differentiating centrally versus peripherally fibrotic granulomas. Furthermore, these findings suggest that heterogeneous cytokine environments exist within granulomas and may be responsible for driving tissue scale morphologies. Using this model we are primed to better understand the complex structure of granulomas, a necessity for developing successful treatments for TB.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A novel approach to identify the ionomer phase in PEMFC by EELS

Proton exchange membrane fuel cells are one of the most promising technologies of energy conversion for both automotive and stationary applications, due to their ultimate cleanness and high efficiency. A critical factor, which strongly affects the fuel cell performance, is the ionomer distribution and coverage over the carbon support, since the catalysts, which are typically Pt and/or Pt-alloy nanoparticles, must be located at the carbon support/ionomer interface to catalyze the sluggish oxygen reduction reaction. However, the characterization and identification of the ionomer film, in terms of ionomer distribution and coverage over the carbon surface, is a long standing challenge. This is because the ionomer film may suffer from beam damage during the characterization, either from x-rays, neutrons or an electron beam, which causes morphological changes. In this regard, we report here a novel approach to identify and differentiate the ionomer, using the carbon signal produced by STEMEELS. Using this approach, not only the ionomer distribution, but also the carbon support distribution, can be probed at high spatial resolutions. In addition, this new approach allows us to identify ionomer-rich and carbon-support rich regions, which are quite challenging to determine using other methods.

08 HYDROGEN↗

DOE OSTI2020

Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Sediment accumulation at culverts involves large-scale and interlinked environmental processes that are difficult to address with experimental or physical modeling methods. This article presents an alternative data-driven investigation for shedding insights into these processes. Accordingly, a web-based geovisual analytics application, the IowaDOT platform, was developed, which allows users to explore the complex processes associated with the sediment deposition at culverts. The platform provides systematic procedures for (1) collecting and integrating analytical variables into a single dataset, (2) quantifying the degree of culvert sedimentation using time series of aerial images, (3) identifying drivers that contribute to culvert sedimentation processes from a variety of culvert structural and upstream landscape characteristics using a tree-based feature selection algorithm, and (4) facilitating the understanding of complex spatial and relational patterns of culvert sedimentation processes using multivariate geovisualizations supported by a self-organizing map (SOM). As the outcomes of this study, these patterns identify culvert sedimentation-prone regions in Iowa and quantify empirical relationships between the drivers and culvert sedimentation degrees. Finally, a simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedbacks are received.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis

Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study—including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes—are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.

14 SOLAR ENERGY↗

DOE OSTI2016

Application of small RNA sequencing to identify microRNAs in acute kidney injury and fibrosis

Establishing a microRNA (miRNA) expression profile in affected tissues provides an important foundation for the discovery of miRNAs involved in the development or progression of pathologic conditions. We conducted small RNA sequencing to generate a temporal profile of miRNA expression in the kidneys using a mouse model of folic acid-induced (250 mg/kg i.p.) kidney injury and fibrosis. From the 103 miRNAs that were differentially expressed over the time course (> 2-fold, p < 0.05), we chose to further investigate miR-18a-5p, which is expressed during the acute stage of the injury; miR-132-3p, which is upregulated during transition between acute and fibrotic injury; and miR-146b-5p, which is highly expressed at the peak of fibrosis. Using qRT-PCR, we confirmed the increased expression of these candidate miRNAs in the folic acid model as well as in other established mouse models of acute injury (ischemia/reperfusion injury) and fibrosis (unilateral ureteral obstruction). In situ hybridization confirmed high expression of miR-18a-5p, miR-132-3p and miR-146b-5p throughout the kidney cortex in mice and humans with severe kidney injury or fibrosis. When primary human proximal tubular epithelial cells were treated with model nephrotoxicants such as cadmium chloride (CdCl{sub 2}), arsenic trioxide, aristolochic acid (AA), potassium dichromate (K{sub 2}Cr{sub 2}O{sub 7}) and cisplatin, miRNA-132-3p was upregulated 4.3-fold after AA treatment and 1.5-fold after K{sub 2}Cr{sub 2}O{sub 7} and CdCl{sub 2} treatment. These results demonstrate the application of temporal small RNA sequencing to identify miR-18a, miR-132 and miR-146b as differentially expressed miRNAs during distinct phases of kidney injury and fibrosis progression. - Highlights: • We used small RNA sequencing to identify differentially expressed miRNAs in kidney. • Distinct patterns were found for acute injury and fibrotic stages in the kidney. • Upregulation of miR-18a, -132 and -146b was confirmed in mice and human kidneys.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

A mouse model of alcoholic liver fibrosis-associated acute kidney injury identifies key molecular pathways

Clinical data strongly indicate that acute kidney injury (AKI) is a critical complication in alcoholic hepatitis, an acute-on-chronic form of liver failure in patients with advanced alcoholic fibrosis. Development of targeted therapies for AKI in this setting is hampered by the lack of an animal model. To enable research into molecular drivers and novel therapies for fibrosis- and alcohol-associated AKI, we aimed to combine carbon tetrachloride (CCl{sub 4})-induced fibrosis with chronic intra-gastric alcohol feeding. Male C57BL/6J mice were administered a low dose of CCl{sub 4} (0.2 ml/kg 2 × week/6 weeks) followed by alcohol intragastrically (up to 25 g/kg/day for 3 weeks) and with continued CCl{sub 4}. We observed that combined treatment with CCl{sub 4} and alcohol resulted in severe liver injury, more pronounced than using each treatment alone. Importantly, severe kidney injury was evident only in the combined treatment group. This mouse model reproduced distinct pathological features consistent with AKI in human alcoholic hepatitis. Transcriptomic analysis of kidneys revealed profound effects in the combined treatment group, with enrichment for damage-associated pathways, such as apoptosis, inflammation, immune-response and hypoxia. Interestingly, Havcr1 and Lcn2, biomarkers of AKI, were markedly up-regulated. Overall, this study established a novel mouse model of fibrosis- and alcohol-associated AKI and identified key mechanistic pathways. - Highlights: • Acute kidney injury (AKI) is a critical complication in alcoholic hepatitis • We developed a novel mouse model of fibrosis- and alcohol-associated AKI • This model reproduces key molecular and pathological features of human AKI • This animal model can help identify new targeted therapies for alcoholic hepatitis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Interrogating the Grainyhead-like 2 ($Grhl2$) genomic locus identifies an enhancer element that regulates palatogenesis in mouse

The highly-conserved Grainyhead-like (Grhl) transcription factors are critical regulators of embryogenesis that regulate cellular survival, proliferation, migration and epithelial integrity, especially during the formation of the craniofacial skeleton. Family member Grhl2 is expressed throughout epithelial tissues during development, and loss of Grhl2 function leads to significant defects in neurulation, abdominal wall closure, formation of the face and fusion of the maxilla/palate. Whereas numerous downstream target genes of Grhl2 have been identified, very little is known about how this crucial developmental transcription factor itself is regulated. Using in silico and in utero expression analyses and functional deletion in mice, we have identified a novel 2.4 kb enhancer element (mm1286) that drives reporter gene expression in a pattern that strongly recapitulates endogenous Grhl2 in the craniofacial primordia, modulates Grhl2expression in these tissues, and augments Grhl2-mediated closure of the secondary palate. Deletion of this genomic element, in the context of inactivation of one allele of Grhl (through generation of double heterozygous Grhl2 +/- ;mm1286 +/- mice), results in a significant predisposition to palatal clefting at birth. Moreover, we found that a highly conserved 325 bp region of mm1286 is both necessary and sufficient for mediating the craniofacial-specific enhancer activity of this region, and that an extremely well-conserved 12-bp sequence within this element (CTGTCAAACAGGT) substantially determines full enhancer function. Together, these data provide valuable new insights into the upstream genomic regulatory landscape responsible for transcriptional control of Grhl2 during palatal closure.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria

Drug discovery faces a crisis. The industry has used up the “obvious” space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. Here, we present proof of concept of an approach to rationally identify a “chemical vocabulary” related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗