Hasil untuk "Chemistry"

Y. Ju, Wenting Sun

R. T. Tung

Zhaohui J. Cai, Anders Bresell, M. H. Steinberg et al.

Purpose The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Patients and methods Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Results Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. Conclusion It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

Haohong Duan, Dingsheng Wang, Yadong Li

J. Moses, A. Moorhouse

R. Wrolstad

W. Murphy

G. Sposito

M. Andreae, P. Crutzen

F. Lea

J. C. Bailar, H. Emeléus, Sir Ronald Nyholm et al.

P. Taylor

J. Handelsman, M. Rondon, S. Brady et al.

Cultured soil microorganisms have provided a rich source of natural-product chemistry. Because only a tiny fraction of soil microbes from soil are readily cultured, soil might be the greatest untapped resource for novel chemistry. The concept of cloning the metagenome to access the collective genomes and the biosynthetic machinery of soil microflora is explored here.

J. McCleverty, T. Meyer

Zhichang Liu, Siva Krishna Mohan Nalluri, J. Stoddart

B. D. de Marco, Bárbara Saú Rechelo, E. G. Tótoli et al.

The growing process of industrialization was a milestone for world economic evolution. Since the 1940s, social movements have revolutionized green chemistry and provided shifts in industrial positions and sustainable processes with advances in environmental impact and awareness of companies and population. Paul Anastas and John Warner, in the 1990s, postulated the 12 principles of Green Chemistry, which are based on the minimization or non-use of toxic solvents in chemical processes and analyzes, as well as, the non-generation of residues from these processes. One of the most active areas of Research and Development in Green Chemistry is the development of analytical methodologies, giving rise to the so-called Green Analytical Chemistry. The impacts of green chemistry on pharmaceutical analyzes, environmental, population, analyst and company are described in this review and they are multidimensional. Every choice and analytical attitude has consequences both in the final product and in everything that surrounds it. The future of green chemistry as well as our future and the environment is also contemplated in this work.

A. Filipa de Almeida, R. Moreira, T. Rodrigues

R. Sheldon, M. Norton

The solution to plastic pollution is not less chemistry but more, greener chemistry in a circular bio-based economy.

Smik Patel, Praveen Jayakumar, Tzu-Ching Yen et al.

Quantum chemistry is among the most promising applications of quantum computing, offering the potential to solve complex electronic structure problems more efficiently than classical approaches. A critical component of any quantum algorithm is the measurement step, where the desired properties are extracted from a quantum computer. This review focuses on recent advancements in quantum measurement techniques tailored for quantum chemistry, particularly within the second quantized framework suitable for current and near-term quantum hardware. We provide a comprehensive overview of measurement strategies developed primarily for the Variational Quantum Eigensolver (VQE) and its derivatives. These strategies address the inherent challenges posed by complexity of the electronic Hamiltonian operator. Additionally, we examine methods for estimating excited states and one- and two-electron properties, extending the applicability of quantum algorithms to broader chemical phenomena. Key aspects of the review include approaches for constructing measurement operators with reduced classical preprocessing and quantum implementation costs, techniques to minimize the number of measurements required for a given accuracy, and error mitigation strategies that leverage symmetries and other properties of the measurement operators. Furthermore, we explore measurement schemes rooted in Quantum Phase Estimation (QPE), which are expected to become viable with the advent of fault-tolerant quantum computing. This review emphasizes foundational concepts and methodologies rather than numerical benchmarks, serving as a resource for researchers aiming to enhance the efficiency and accuracy of quantum measurements in quantum chemistry.

Alan Bidart, Prateek Vaish, Tilas Kabengele et al.

Quantum computing offers the promise of revolutionizing quantum chemistry by enabling the solution of chemical problems for substantially less computational cost. While most demonstrations of quantum computation to date have focused on resolving the energies of the electronic ground states of small molecules, the field of quantum chemistry is far broader than ground state chemistry; equally important to practicing chemists are chemical reaction dynamics and reaction mechanism prediction. Here, we review progress toward and the potential of quantum computation for understanding quantum chemistry beyond the ground state, including for reaction mechanisms, reaction dynamics, and finite temperature quantum chemistry. We discuss algorithmic and other considerations these applications share, as well as differences that make them unique. We also highlight the potential speedups these applications may realize and challenges they may face. We hope that this discussion stimulates further research into how quantum computation may better inform experimental chemistry in the future.