Hasil untuk "Chemistry"

L. Feldman, J. Mayer, M. Grasserbauer

M. Caton, K. Crowshaw

A. Alexakis*, J. Bäckvall, N. Krause et al.

W. Pogozelski, T. Tullius

A. Nakamura, S. Ito, K. Nozaki

R. Kozma, J. W. Russell

P. Dervan

Vinod Kumar, Kamalneet Kaur, G. Gupta et al.

K. V. van Bommel, A. Friggeri, S. Shinkai

V. Bloomfield, D. Crothers, I. Tinoco

S. Pandey

Simone Brogi, T. Ramalho, K. Kuča et al.

Department of Pharmacy, University of Pisa, Pisa, Italy, 2 Laboratory of Molecular Modeling, Chemistry Department, Federal University of Lavras, Lavras, Brazil, Department of Chemistry, Faculty of Science, University of Hradec Kralove, Kralove, Czechia, DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City, Mexico, 5 Faculty of Chemical and Food Technology, Slovak Technical University, Bratislava, Slovakia

A. Legon

Michael Cuccarese

Activity cliff prediction - identifying positions where small structural changes cause large potency shifts - has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" - where small modifications cause disproportionately large effects, as captured by SALI normalization - requires an 11-feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff-prone position first 53% of the time (vs. 27% random - 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 - a 31% reduction in first-round experiments. Predicting which modification to make is not tractable from structure alone (Spearman 0.268, collapsing to -0.31 on novel scaffolds). The system is released as open-source code and an interactive webapp.

Yingtao Zhang, Wenhe Li, Yang Wu et al.

The leakage detection of oil and gas is very important for the safe operation of pipelines. The existing working condition recognition methods have limitations in processing and capturing complex multi-category leakage signal characteristics. In order to improve the accuracy of oil and gas pipeline leakage detection, a multi-scale convolutional neural network-Transformer (MSCNN-Transformer)-based oil and gas pipeline leakage condition recognition method is proposed. Firstly, in order to capture the global information and nonlinear characteristics of the time series signal, STFT is used to generate the time-frequency image. Furthermore, in order to enrich the feature information from different dimensions, the one-dimensional signal and the two-dimensional time-frequency image are sampled by multi-scale convolution, and the global relationship is established by combining the multi-head attention mechanism of the Transformer module. Finally, the leakage signal is accurately identified by fusing features and classifiers. The experimental results show that the proposed method shows high performance on the GPLA-12 data set, and the recognition accuracy is 96.02%. Compared with other leakage signal recognition methods, the proposed method has obvious advantages.

André Guaraci DeVito-Moraes, Isabela Souza Vardasca, Miguel Peñarrocha-Diago et al.

This study investigates the mechanical and optical properties of monolithic zirconia used in dentistry, focusing on how different concentrations of yttrium oxide and varied sintering times affect the material. A critical trade-off in ceramics has been reported in the literature, in which increased crystalline content (like in zirconia) leads to higher mechanical strength but lower aesthetic translucency. However, detailed information on this trade-off process for different types of zirconia is lacking. A total of seven types of zirconia varying in yttria content (3 mol% to 5 mol%) were tested across four sintering protocols available in a laboratory zirconia sintering device: Slow (12 h), Standard (8 h), Fast (3.5 h), and Ultrafast (1.15 h). The primary findings indicate that while a higher yttria concentration correlates with lower flexural strength and high translucency, the sintering time generally did not compromise mechanical strength or color variation across most samples. Nevertheless, the Fast and Ultrafast protocols did significantly reduce the translucency of zirconia with a high concentration of yttrium oxide.

G. Lahm, Daniel Cordova, James D. Barry

Robert L C Voeten, I. Ventouri, R. Haselberg et al.

Robert L. C. Voeten,†,‡ Iro K. Ventouri,‡,§ Rob Haselberg,*,† and Govert W. Somsen† †Division of BioAnalytical Chemistry, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands ‡TI-COAST, Science Park 904, 1098 XH Amsterdam, The Netherlands Analytical Chemistry Group, van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands

I. Khan, Aliya Ibrar, N. Abbas et al.

N. A. Moroz, K. S. Tikhonov, L. V. Gerasimov et al.

The permutation symmetry is a fundamental attribute of the collective wavefunction of indistinguishable particles. It makes a difference for the behavior of collective systems having different quantum statistics but existing in the same environment. Here we show that for some specific quantum conjugation between the spin and spatial degrees of freedom the indistinguishable particles can behave similarly for either quantum statistics. In particular, a mesoscopically scaled collection of atomic qubits, mediated by optical tweezers, can model the behavior of a valent electronic shell compounded with nuclear centers in molecules. This makes possible quantum simulations of mono and divalent bonds in quantum chemistry by manipulation of up to four bosonic atoms confined with optical microtraps.