2,738 research outputs found
Fine Splitting in Charmonium Spectrum with Channel Coupling Effect
We study the fine splitting in charmonium spectrum in quark model with the
channel coupling effect, including , , and ,
, channels. The interaction for channel coupling is
constructed from the current-current Lagrangian related to the color
confinement and the one-gluon exchange potentials. By adopting the massive
gluon propagator from the lattice calculation in the nonperturbative region,
the coupling interaction is further simplified to the four-fermion interaction.
The numerical calculation still prefers the assignment of X(3872).Comment: Submitted to Chinese Physics
Simultaneous determination of captopril and hydrochlorothiazide by time-resolved chemiluminescence with artificial neural network calibration
AbstractThe combined use of chemometrics and chemiluminescence (CL) measurements, with the aid of the stopped-flow mixing technique, developed a simple time-resolved CL method for the simultaneous determination of captopril (CPL) and hydrochlorothiazide (HCT). The stopped-flow technique in a continuous-flow system was employed in this work in order to emphasize the kinetic differences between the two analytes in cerium (IV)-rhodamine 6G CL system. After the flow was stopped, an initial rise of CL signal was observed for HCT standards, while a direct decay of CL signal was obtained for CPL standards. The mixed CL signal was monitored and recorded on the whole process of continuousflow/stopped-flow, and the obtained data were processed by the chemometric approach of artificial neural network. The relative prediction error (RPE) of CPL and HCT was 5.9% and 8.7%, respectively. The recoveries of CPL and HCT in tablets were found to fall in the range between 95% and 106%. The proposed method was successfully applied to the simultaneous determination of CPL and HCT in a compound pharmaceutical formulation
An open unified deep graph learning framework for discovering drug leads
Computational discovery of ideal lead compounds is a critical process for
modern drug discovery. It comprises multiple stages: hit screening, molecular
property prediction, and molecule optimization. Current efforts are disparate,
involving the establishment of models for each stage, followed by multi-stage
multi-model integration. However, this is non-ideal, as clumsy integration of
incompatible models increases research overheads, and may even reduce success
rates in drug discovery. Facilitating compatibilities requires establishing
inherent model consistencies across lead discovery stages. Towards that effect,
we propose an open deep graph learning (DGL) based pipeline: generative
adversarial feature subspace enhancement (GAFSE), which first unifies the
modeling of these stages into one learning framework. GAFSE also offers
standardized modular design and streamlined interfaces for future expansions
and community support. GAFSE combines adversarial/generative learning, graph
attention network, graph reconstruction network, and optimizes the
classification/regression loss, adversarial/generative loss, and reconstruction
loss simultaneously. Convergence analysis theoretically guarantees model
generalization performance. Exhaustive benchmarking demonstrates that the GAFSE
pipeline achieves excellent performance across almost all lead discovery
stages, while also providing valuable model interpretability. Hence, we believe
this tool will enhance the efficiency and productivity of drug discovery
researchers.Comment: This article is used as the preliminary studies for the application
of Lee Kuan Yew Postdoctoral Fellowship (LKYPDF) 2023 in Singapore. All
rights reserve
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