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Results for “compound screening”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Inhibitors of Mycobacterium tuberculosis EgtD target both substrate binding sites to limit hercynine production

Ergothioneine (EGT) is a low molecular weight histidine betaine essential in all domains of life but only synthesized by selected few organisms. Synthesis of EGT by Mycobacterium tuberculosis (M. tb) is critical for maintaining bioenergetic homeostasis and protecting the bacterium from alkylating agents, oxidative stress, and anti-tubercular drugs. EgtD, an S-adenosylmethionine-dependent methyltransferase (AdoMet), catalyzes the trimethylation of L-Histidine to initiate EGT biosynthesis and this reaction has been shown to be essential for EGT production in mycobacteria and for long-term infection of murine macrophages by M. tb. In this work, library screening and structure-guided strategies identified multiple classes of M. tb EgtD inhibitors that bind in various regions of the enzyme active site. X-ray crystal structures of EgtD-inhibitor complexes confirm that L-Histidine analogs bind solely to the L-Histidine binding site while drug-like inhibitors, such as TGX-221, and S-Glycyl-H-1152 span both the L-Histidine and AdoMet binding sites. These enzyme-inhibitor complexes provide detailed structural information of compound scaffolds useful for developing more potent inhibitors that could shorten Tuberculosis treatment regimens by weakening important bacterial defenses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Discovery of first-in-class inhibitors of ASH1L histone methyltransferase with anti-leukemic activity

ASH1L histone methyltransferase plays a crucial role in the pathogenesis of different diseases, including acute leukemia. While ASH1L represents an attractive drug target, developing ASH1L inhibitors is challenging, as the catalytic SET domain adapts an inactive conformation with autoinhibitory loop blocking the access to the active site. Here, by applying fragment-based screening followed by medicinal chemistry and a structure-based design, we developed first-in-class small molecule inhibitors of the ASH1L SET domain. The crystal structures of ASH1L-inhibitor complexes reveal compound binding to the autoinhibitory loop region in the SET domain. When tested in MLL leukemia models, our lead compound, AS-99, blocks cell proliferation, induces apoptosis and differentiation, downregulates MLL fusion target genes, and reduces the leukemia burden in vivo. This work validates the ASH1L SET domain as a druggable target and provides a chemical probe to further study the biological functions of ASH1L as well as to develop therapeutic agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to NO 3 - pollution in wastewater by converting it to innocuous N 2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. Here, in response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO 3 - reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N 2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reversible redox chemistry in azobenzene-based organic molecules for high-capacity and long-life nonaqueous redox flow batteries

Redox flow batteries (RFBs), often categorized as aqueous and nonaqueous systems, represent a type of large-scale energy storage technology for renewable energy resources. Redox-active organic molecules have recently drawn extensive interests in RFBs as promising active materials due to their elemental abundance, structural diversity and high tunability of properties, but employing organic molecules in nonaqueous systems is far limited in terms of useable capacity and cycling stability. Here we introduce a class of azobenzene-based organic compounds with the azo group as redox-active center in the p-conjugated structure as new active materials to realize high-performance nonaqueous RFBs with long cycling life and high capacity. By screening suitable organic solvents with high solubility and conductivity, the azobenzene organic molecule was capable to achieve a stable long cycling with a low capacity decay of 0.014% per cycle and 0.16% per day over 1000 cycles. And the stable cycling of electrolytes under a high concentration of 1 M was also realized, delivering a high reversible capacity of ~46 Ah L -1 . The unique lithium-coupled redox chemistry accompanied with a voltage increase was observed and revealed by experimental characterization and theoretical simulation. With the reversible two-electron redox activity of azo group in p-conjugated structures, azobenzene as an azo-aromatic molecule represents a class of promising redox-active organics for potential grid-scale energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Suspect and Nontarget Screening for Contaminants of Emerging Concern in an Urban Estuary

This study used suspect and nontarget screening with high-resolution mass spectrometry to characterize the occurrence of contaminants of emerging concern (CECs) in the nearshore marine environment of Puget Sound (WA). In total, 87 non-polymeric CECs were identified; those confirmed with reference standards (45) included pharmaceuticals, herbicides, vehicle-related compounds, plasticizers, and flame retardants. Eight polyfluoroalkyl substances were detected; perfluorooctanesulfonic acid (PFOS) concentrations were as high as 72-140 ng/L at one location. Low levels of methamphetamine were detected in 41% of the samples. Transformation products of pesticides were tentatively identified, including two novel transformation products of tebuthiuron. While a hydrodynamic simulation, analytical results, and dilution calculations demonstrated the prevalence of wastewater effluent to nearshore marine environments, the identity and abundance of selected CECs revealed the additional contributions from stormwater and localized urban and industrial sources. For the confirmed CECs, risk quotients were calculated based on concentrations and predicted toxicities, and eight CECs had risk quotients >1. Dilution in the marine estuarine environment lowered the risks of most wastewater-derived CECs, but dilution alone is insufficient to mitigate risks of localized inputs. These findings highlighted the necessity of suspect and nontarget screening and revealed the importance of localized contamination sources in urban marine environments.

Tian, Zhenyu↗
DOE OSTI2023

Ab initio calculation of carrier mobility in semiconductors including ionized-impurity scattering

The past decade has seen the emergence of ab initio computational methods for calculating phonon-limited carrier mobilities in semiconductors with predictive accuracy. More realistic calculations ought to take into account additional scattering mechanisms such as, for example, impurity and grain-boundary scattering. Here, in this paper, we investigate the effect of ionized-impurity scattering on the carrier mobility. We model the analytical impurity potential parameterized from first principles by a collection of randomly distributed Coulomb scattering centers, and we include this relaxation channel into the ab initio Boltzmann transport equation, as implemented in the EPW code. We demonstrate this methodology by considering silicon, silicon carbide, and gallium phosphide, for which detailed experimental data are available. Our calculations agree well with experiments over a broad range of temperatures and impurity concentrations. For each compound investigated here, we compare the relative importance of electron-phonon scattering and ionized-impurity scattering, and we critically assess the reliability of Matthiessen's rule. We also show that an accurate description of dielectric screening and carrier effective masses can improve quantitative agreement with experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗
DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗
DOE OSTI2024

Detection and Screening of Organic Contaminants in A Riverine System of Georgia Using Non-Targeted Analysis

Numerous organic chemicals exist within aquatic environments, yet effectively screening and prioritizing them is a huge challenge. This study provides a comprehensive investigation into the ecological dynamics of the North Oconee River within Athens-Clarke County, Georgia, with a specific focus on the distribution of 33 identified compounds, including a prominent pesticide. The research, conducted in the riverine ecosystems proximal to the Firefly trail, employs advanced analytical techniques to elucidate potential contamination sources arising from agricultural and urban runoff. Intriguingly, the study reveals North Oconee River near the Firefly Trail as a notable site for heightened pesticide contamination, warranting a meticulous exploration of its origins. Furthermore, the investigation unveils the intricate microbial degradation processes of malathion within the North Oconee River, elucidating the pivotal role played by microbial activity in river water. The detection of degradant byproducts prompts the considerations of bioavailability and toxicity, associating potential implications for the river’s overall ecological health. Ongoing research endeavors to precisely quantify environmental risks and unravel indigenous microbial degradation pathways, presenting pivotal contributions to the scientific community’s understanding of complex riverine ecosystems. This research serves as a foundational piece in informing sustainable environmental management practices and emphasizes the urgency of comprehensive stewardship in safeguarding aquatic ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Garcinoic Acid Is a Natural and Selective Agonist of Pregnane X Receptor

Pregnane X receptor (PXR) is a master xenobiotic-sensing transcription factor and a validated target for immune and inflammatory diseases. The identification of chemical probes to investigate the therapeutic relevance of the receptor is still highly desired. In fact, currently available PXR ligands are not highly selective and can exhibit toxicity and/or potential off-target effects. In this study, we have identified garcinoic acid as a selective and efficient PXR agonist. The properties of this natural molecule as a specific PXR agonist were demonstrated by the screening on a panel of nuclear receptors, the assessment of the physical and thermodynamic binding affinity, and the determination of the PXR-garcinoic acid complex crystal structure. Cytotoxicity, transcriptional, and functional properties were investigated in human liver cells, and compound activity and target engagement were confirmed in vivo in mouse liver and gut tissue. In conclusion, garcinoic acid is a selective natural agonist of PXR and a promising lead compound toward the development of new PXR-regulating modulators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Label-free affinity screening, design and synthesis of inhibitors targeting the Mycobacterium tuberculosis L-alanine dehydrogenase

The ability of Mycobacterium tuberculosis (Mtb) to persist in its host may enable an evolutionary advantage for drug resistant variants to emerge. A potential strategy to prevent persistence and gain drug efficacy is to directly target the activity of enzymes that are crucial for persistence. We present a method for expedited discovery and structure-based design of lead compounds by targeting the hypoxia-associated enzyme L-alanine dehydrogenase (AlaDH). Biochemical and structural analyses of AlaDH confirmed binding of nucleoside derivatives and showed a site adjacent to the nucleoside binding pocket that can confer specificity to putative inhibitors. Using a combination of dye-ligand affinity chromatography, enzyme kinetics and protein crystallographic studies, we show the development and validation of drug prototypes. Crystal structures of AlaDH-inhibitor complexes with variations at the N6 position of the adenyl-moiety of the inhibitor provide insight into the molecular basis for the specificity of these compounds. We describe a drug-designing pipeline that aims to block Mtb to proliferate upon re-oxygenation by specifically blocking NAD accessibility to AlaDH. The collective approach to drug discovery was further evaluated through in silico analyses providing additional insight into an efficient drug development strategy that can be further assessed with the incorporation of in vivo studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High-Throughput Screening of Strong Electron–Phonon Couplings in Ternary Metal Diborides

Here, we perform a high-throughput screening on phonon-mediated superconductivity in a ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron–phonon coupling, 43 compounds are revealed to show electron–phonon coupling strength higher than that of MgB 2 . An anticorrelation between the energetic stability and electron–phonon coupling strength is identified. We suggest two phases, i.e., Li 3 ZrB 8 and Ca 3 YB 8 , to be synthesized, which show reasonable energetic stability and superconducting critical temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerating Solvent Selection for Type II Porous Liquids

Type II porous liquids, comprising intrinsically porous molecules dissolved in a liquid solvent, potentially combine the adsorption properties of porous adsorbents with the handling advantages of liquids. Previously, discovery of appropriate solvents to make porous liquids had been limited to direct experimental tests. We demonstrate an efficient screening approach for this task that uses COSMO-RS calculations, predictions of solvent pK a values from a machine-learning model, and several other features and apply this approach to select solvents from a library of more than 11,000 compounds. Additionally, this method is shown to give qualitative agreement with experimental observations for two molecular cages, CC13 and TG-TFB-CHEDA, identifying solvents with higher solubility for these molecules than had previously been known. Ultimately, the algorithm streamlines the downselection of suitable solvents for porous organic cages to enable more rapid discovery of Type II porous liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Aryl hydrocarbon receptor-dependent toxicity by retene requires metabolic competence

Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds frequently detected in the environment with widely varying toxicities. Many PAHs activate the aryl hydrocarbon receptor (AHR), inducing the expression of a battery of genes, including xenobiotic metabolizing enzymes like cytochrome P450s (CYPs); however, not all PAHs act via this mechanism. We screened several parent and substituted PAHs in in vitro AHR activation assays to classify their unique activity. Retene (1-methyl-7-isopropylphenanthrene) displays Ahr2-dependent teratogenicity in zebrafish, but did not activate human AHR or zebrafish Ahr2, suggesting a retene metabolite activates Ahr2 in zebrafish to induce developmental toxicity. To investigate the role of metabolism in retene toxicity, studies were performed to determine the functional role of cyp1a, cyp1b1, and the microbiome in retene toxicity, identify the zebrafish window of susceptibility, and measure retene uptake, loss, and metabolite formation in vivo. Cyp1a-null fish were generated using CRISPR-Cas9. Cyp1a-null fish showed increased sensitivity to retene toxicity, whereas Cyp1b1-null fish were less susceptible, and microbiome elimination had no significant effect. Zebrafish required exposure to retene between 24 and 48 hours post fertilization (hpf) to exhibit toxicity. After static exposure, retene concentrations in zebrafish embryos increased until 24 hpf, peaked between 24 and 36 hpf, and decreased rapidly thereafter. We detected retene metabolites at 36 and 48 hpf, indicating metabolic onset preceding toxicity. This study highlights the value of combining molecular and systems biology approaches with mechanistic and predictive toxicology to interrogate the role of biotransformation in AHR-dependent toxicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗