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Relevant bibliographies by topics / Chimical Diversity

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Journal articles

Academic literature on the topic 'Chimical Diversity'

Author: Grafiati

Published: 9 March 2024

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Journal articles on the topic "Chimical Diversity"

1

Awale, Mahendra, Ricardo Visini, Daniel Probst, Josep Arús-Pous, and Jean-Louis Reymond. "Chemical Space: Big Data Challenge for Molecular Diversity." CHIMIA International Journal for Chemistry 71, no. 10 (2017): 661–66. http://dx.doi.org/10.2533/chimia.2017.661.

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2

Schröder, Jens A., and Pauline E. Jullien. "The Diversity of Plant Small RNAs Silencing Mechanisms." CHIMIA International Journal for Chemistry 73, no. 5 (2019): 362–67. http://dx.doi.org/10.2533/chimia.2019.362.

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Small RNAs gene regulation was first discovered about 20 years ago. It represents a conserve gene regulation mechanism across eukaryotes and is associated to key regulatory processes. In plants, small RNAs tightly regulate development, but also maintain genome stability and protect the plant against pathogens. Small RNA gene regulation in plants can be divided in two canonical pathways: Post-transcriptional Gene Silencing (PTGS) that results in transcript degradation and/or translational inhibition or Transcriptional Gene Silencing (TGS) that results in DNA methylation. In this review, we will

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3

Fabrizio, Alberto, Ksenia Briling, Andrea Grisafi, and Clémence Corminboeuf. "Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity." CHIMIA International Journal for Chemistry 74, no. 4 (2020): 232–36. http://dx.doi.org/10.2533/chimia.2020.232.

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Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density functional theory, the electron density is a particularly compelling target for non-linear regression. Nevertheless, the scalability and the transferability of the existing machine-learning models of ρ(r) are limited by its complex rotational symmetries. Recently, in collaboration with Ceriotti and coworkers, we combined an efficient electro

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4

Wolfender, Jean-Luc, and Emerson Ferreira Queiroz. "Chemical Diversity of Natural Resources and the Bioactivity of their Constituents." CHIMIA International Journal for Chemistry 66, no. 5 (2012): 324–29. http://dx.doi.org/10.2533/chimia.2012.324.

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5

Ila, Hiriyakkanavar, and Hiriyakkanavar Junjappa. "Molecular Diversity through Novel Organosulfur Synthons: Versatile Templates for Heterocycle Synthesis." CHIMIA International Journal for Chemistry 67, no. 1 (2013): 17–22. http://dx.doi.org/10.2533/chimia.2013.17.

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Kang, Chengjun, and Andrei Honciuc. "A Diversity of Asymmetric Nano-/Microcolloidal Architectures Grown by ATRP from Janus Seeds." CHIMIA International Journal for Chemistry 73, no. 4 (2019): 324–28. http://dx.doi.org/10.2533/chimia.2019.324.

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7

Muñiz, Manuel, and Auxiliadora Aguilera-Romero. "A Role for Lipids in Protein Sorting?" CHIMIA 75, no. 12 (2021): 1026. http://dx.doi.org/10.2533/chimia.2021.1026.

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Lipid and protein diversity provides structural and functional identity to the membrane compartments that define the eukaryotic cell. This compositional heterogeneity is maintained by the secretory pathway, which feeds newly synthesized proteins and lipids to the endomembrane systems. The precise sorting of lipids and proteins through the pathway guarantees the achievement of their correct delivery. Although proteins have been shown to be key for sorting mechanisms, whether and how lipids contribute to this process is still an open discussion. Our laboratory, in collaboration with other groups

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8

Probst, Daniel. "The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry." CHIMIA 77, no. 1/2 (2023): 56. http://dx.doi.org/10.2533/chimia.2023.56.

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While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding, the exclusiveness of research, and thus the inequality between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community that c

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9

Wennemers, Helma. "Peptides – Molecular Allrounders." CHIMIA International Journal for Chemistry 75, no. 6 (2021): 525–29. http://dx.doi.org/10.2533/chimia.2021.525.

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The enormous structural and functional diversity available through combining different amino acids into peptides offers numerous exciting opportunities. This article summarizes recent research highlights from my laboratory in the areas of asymmetric catalysis, supramolecular chemistry, and chemical biology. This scope includes the development of bioinspired peptide catalysts, synthetic collagen peptides, supramolecular porous assemblies, and cell-penetrating peptides.

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10

Simon, Clémence, Suihan Feng, and Howard Riezman. "Chemical Biology Tools to Study Lipids and their Metabolism with Increased Spatial and Temporal Resolution." CHIMIA 75, no. 12 (2021): 1012. http://dx.doi.org/10.2533/chimia.2021.1012.

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Lipids are important cellular components providing many essential functions. To fulfill these various functions evolution has selected for a diverse set of lipids and this diversity is seen at the organismal, cellular and subcellular level. Understanding how cells maintain this complex lipid organization is a very challenging problem, which for lipids, is not easily addressed using biochemical and genetic techniques. Therefore, chemical tools have an important role to play in our quest to understand the complexities of lipid metabolism. Here we discuss new chemical tools to study lipids, their

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