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Results for “quantitative structure-activity relationship”

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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10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Unveiling decaying mechanism through quantitative structure-activity relationship in electrolytes for lithium-ion batteries

Understanding the decaying mechanism in lithium-ion batteries (LIBs) is critical to establishing a stable electrolyte system. Despite the advent of various novel electrolyte solvents designated for high-voltage LIBs, their working principles are not fully understood. Currently, oxidative decomposition of electrolytes is believed to be the major cause of capacity fade, and tremendous effort has been devoted to discovering a new electrolyte with enhanced anodic stability. However, the oxidative decomposition process cannot solely explain the rapid decay of some electrolyte systems with intrinsic high anodic stability when used with a high-nickel layered oxide cathode such as LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622). In this report, a study of the quantitative structure-activity relationship was conducted to deepen the mechanistic understanding of the decay in high-voltage LIBs. Here, the results obtained from the newly introduced molecular pair analysis and linear free-energy relationship (LFER) studies were highly consistent with the solvation-involved decaying mechanism in a high-nickel layered oxide cathode cycling at high voltage (> 4.5 V vs. Li/Li + ). There was no evidence correlating the solvation ability of electrolyte solvents with the decay of a high-nickel layered oxide cathode cycling at a relatively low voltage (< 4.3 V vs. Li/Li + ), nor with the high-voltage spinel cathode LiNi 0.5 Mn 1.5 O 4 (LNMO). Undoubtedly, the unveiled mechanistic insight provides a critical guideline for the development of an appropriate electrolyte system targeting different high-voltage cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Structure-activity correlations for organophosphorus ester anticholinesterases. Part 2: CNDO/2 calculations applied to ester hydrolysis rates

Quantitative structure-activity relationships are presented for the hydrolysis of organophosphorus esters, RR'P(O)X, where R and R' are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. CNDO/2 calculations provide values for molecular parameters that correlate with alkaline hydrolysis rates. For each subset of esters with the same leaving group, X, the CNDO-derived net atomic charge at the central phosphorus atom correlates well with the alkaline hydrolysis rate constants. For the whole set of esters with different leaving groups, equations are derived that relate charge, orbital energy and bond order to the hydrolysis rate constants.

Johnson, H.↗
NASA NTRS1985

QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis

Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of catonic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR'P(O)X, where R and R' are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrogen hydrolysis rate constant.

Johnson, H.↗
NASA NTRS1985

QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis

Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

Johnson, H.↗
NASA NTRS2000

Development and Application of Computational/In Vitro Toxicological Methods for Chemical Hazard Risk Reduction of New Materials for Advanced Weapon Systems

The development of quantitative structure-activity relationship (QSAR) is essential for reducing the chemical hazards of new weapon systems. The current collaboration between HEST (toxicology research and testing), MLPJ (computational chemistry) and PRS (computational chemistry, new propellant synthesis) is focusing R&D efforts on basic research goals that will rapidly transition to useful products for propellant development. Computational methods are being investigated that will assist in forecasting cellular toxicological end-points. Models developed from these chemical structure-toxicity relationships are useful for the prediction of the toxicological endpoints of new related compounds. Research is focusing on the evaluation tools to be used for the discovery of such relationships and the development of models of the mechanisms of action. Combinations of computational chemistry techniques, in vitro toxicity methods, and statistical correlations, will be employed to develop and explore potential predictive relationships; results for series of molecular systems that demonstrate the viability of this approach are reported. A number of hydrazine salts have been synthesized for evaluation. Computational chemistry methods are being used to elucidate the mechanism of action of these salts. Toxicity endpoints such as viability (LDH) and changes in enzyme activity (glutahoione peroxidase and catalase) are being experimentally measured as indicators of cellular damage. Extrapolation from computational/in vitro studies to human toxicity, is the ultimate goal. The product of this program will be a predictive tool to assist in the development of new, less toxic propellants.

Frazier, John M.↗
DOE OSTI2021

Probing Active Sites in Cu x Pd y Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy

Size-selected clusters are important model catalysts because of their narrow size and compositional distributions, as well as enhanced activity and selectivity in many reactions. Still, their structure-activity relationships are, in general, elusive. The main reason is the difficulty in identifying and quantitatively characterizing the catalytic active site in the clusters when it is confined within subnanometric dimensions and under the continuous structural changes the clusters can undergo in reaction conditions. Using machine learning approaches for analysis of the operando X-ray absorption near-edge structure spectra, we obtained accurate speciation of the Cu x Pd y cluster types during the propane oxidation reaction and the structural information about each type. As a result, we elucidated the information about active species and relative roles of Cu and Pd in the clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Nucleotides as nucleophiles: reactions of nucleotides with phosphoimidazolide activated guanosine

An earlier study of the reaction of phosphoimidazolide activated nucleosides (ImpN) in aqueous phosphate buffers indicated two modes of reaction of the phosphate monoanion and dianion. The first mode is catalysis of the hydrolysis of the P-N bond in ImpN's which leads to imidazole and nucleoside 5'-monophosphate. The second represents a nucleophilic substitution of the imidazole to yield the nucleoside 5'-diphosphate. This earlier study thus served as a model for the reaction of ImpN with nucleoside monophosphates (pN) because the latter can be regarded as phosphate derivatives. In the present study we investigated the reaction of guanosine 5'-phosphate-2-methylimidazolide, 2-MeImpG, in the presence of pN (N = guanosine, adenosine and uridine) in the range 6.9 less than or equal to pH less than or equal to 7.7. We observed that pN's do act as nucleophiles to form NppG, and as general base to enhance the hydrolysis of the P-N bond in 2-MeImpG, i.e. pN show the same behavior as inorganic phosphate. The kinetic analysis yields the following rate constants for the dianion pN2-: knpN = 0.17 +/- 0.02 M-1 h-1 for nucleophilic attack and khpN = 0.11 +/- 0.07 M-1 h-1 for general base catalysis of the hydrolysis. These rate constants which are independent of the nucleobase compare with kp.2 = 0.415 M-1 h-1 and khp2. = 0.217 M-1 h-1 for the reactions of HPO4(2-). In addition, this study shows that under conditions where pN presumably form stacks, the reaction mechanism remains unchanged although in quantitative terms stacked pN are somewhat less reactive. Attack by the 2'-OH and 3'-OH groups of the ribose moiety in amounts greater than or equal to 1% is not observed; this is attributed to the large difference in nucleophilicity in the neutral pH range between the phosphate group and the ribose hydroxyls. This nucleophilicity rank is not altered by stacking.

NASA Discipline Exobiology↗