Search NASA⌕ Search

SEARCH · Search NASA

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

4 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↗
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↗