Hasil untuk "Chemistry"

Xinliang Li, Zhaodong Huang, C. Shuck et al.

P. Nowak, Renata Wietecha-Posłuszny, J. Pawliszyn

Abstract The concept of White Analytical Chemistry (WAC) is presented as an extension of Green Analytical Chemistry (GAC). We propose the 12 WAC principles as an alternative to the known 12 GAC principles. In addition to green aspects, WAC takes into account other key criteria affecting the quality of the method, analytical (red) and practical (blue). In reference to the RGB color model, according to which mixing of red, green and blue light beams gives the impression of whiteness, a white analytical method shows the coherence and synergy of the analytical, ecological and practical attributes. Whiteness can also be quantified, based on the assessment of individual principles, as a convenient parameter useful in comparisons and selecting optimal method. WAC is closer to the idea of sustainable development due to a more holistic view, as it strives for a compromise that avoids an unconditional increase in greenness at the expense of functionality.

Yudong Cao, J. Romero, J. Olson et al.

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

Sam McArdle, Suguru Endo, Alán Aspuru-Guzik et al.

With small quantum computers becoming a reality, first applications are eagerly sought. Quantum chemistry presents a spectrum of computational problems, from relatively easy to classically intractable. Algorithms for the easiest of these have been run on the first quantum computers. But an urgent question is, how well will these algorithms scale to go beyond what is possible classically? This review presents strategies employed to construct quantum algorithms for quantum chemistry, with the goal that quantum computers will eventually answer presently inaccessible questions, for example, in transition metal catalysis or important biochemical reactions.

S. Godtfredsen, Michael G. Wityshyn, Acds Useng et al.

Kathryn M. Nelson, Jayme L. Dahlin, J. Bisson et al.

Curcumin is a constituent (up to ∼5%) of the traditional medicine known as turmeric. Interest in the therapeutic use of turmeric and the relative ease of isolation of curcuminoids has led to their extensive investigation. Curcumin has recently been classified as both a PAINS (pan-assay interference compounds) and an IMPS (invalid metabolic panaceas) candidate. The likely false activity of curcumin in vitro and in vivo has resulted in >120 clinical trials of curcuminoids against several diseases. No double-blinded, placebo controlled clinical trial of curcumin has been successful. This manuscript reviews the essential medicinal chemistry of curcumin and provides evidence that curcumin is an unstable, reactive, nonbioavailable compound and, therefore, a highly improbable lead. On the basis of this in-depth evaluation, potential new directions for research on curcuminoids are discussed.

Patrick Reiser, Marlen Neubert, Andr'e Eberhard et al.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs. Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

J. Irwin, T. Sterling, Michael M. Mysinger et al.

ZINC is a free public resource for ligand discovery. The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biological activity, physical property, vendor, catalog number, name, and CAS number. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

H. Werner, P. Knowles, G. Knizia et al.

Yan Zhao, D. Truhlar

Sophie Purser, Peter R. Moore, S. Swallow et al.

D. Breck

R. Marcus, N. Sutin

A. Katritzky, C. Rees, E. Scriven

CHEC III is organized in 15 Volumes and closely follows the organization used in the previous edition: Volumes 1 and 2: Cover respectively three- and four-membered heterocycles, together with all fused systems containing a three- or four-membered heterocyclic ring. Volume 3: Five-membered rings with one heteroatom together with their benzo- and other carbocyclic-fused derivatives. Volumes 4, 5 and 6: Cover five-membered rings with two heteroatoms, and three or more heteroatoms, respectively, each with their fused carbocyclic compounds. Volumes 7, 8 and 9: Dedicated to six-membered rings with one, two, and more than two heteroatoms, respectively, again with the corresponding fused carbocylic compounds. Volumes 10, 11 and 12: Cover systems containing at least two directly fused heterocyclic five- and/or six-membered rings: of these Volume 10 deals with bi-heterocyclic rings without a ring junction heteroatom, and Volume 11 deals with 5:5 and 5:6 fused rings systems with at least one ring junction nitrogen, while Volume 12 is devoted to all other systems of five and/or six-membered fused or spiro heterocyclic rings with ring junction heteroatoms. Volumes 13 and 14: Seven-membered and larger heterocyclic rings including all their fused derivatives (except those containing three- or four-membered heterocyclic rings which are included in Volume 1 and 2, respectively). Volume 15: Author, ring and subject indexes.

C. Burda, Xiaobo Chen, R. Narayanan et al.

J. Szejtli

A. Vogel, B. Furniss

D. Tasis, N. Tagmatarchis, A. Bianco et al.

J. Miller, James N. Miller, P. Worsfold