Hasil untuk "Chemistry"

Idalia Bilecka, M. Niederberger

V. Polshettiwar, R. Varma

B. Cravatt, A. Wright, J. Kozarich

M. Simon, Chao‐Jun Li

D. Constable, P. Dunn, J. Hayler et al.

S. Schreiber

J. Collman, L. Hegedus

L. Hammett

Dave R van Staveren, N. Metzler‐Nolte

B. J. Aylett

E. Abel, F. Stone, G. Wilkinson et al.

R. E. Call

Stephen Y. Lin, C. Dence

H. Timmerman, A. Bast, H. Goot et al.

D. Jacob

R. Crabtree

K. Klabunde

G. Baffou, R. Quidant

G. Maggiora, Martin Vogt, Dagmar Stumpfe et al.

Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.

Ana Paula Yumi Nishimura, Fernando Augusto Pedersen Voll, Nadia Krieger et al.

Kinetic models are important tools for guiding the design and optimization of lipase-catalyzed processes. These processes follow the Ping Pong bi bi mechanism, for which mechanistic kinetic equations can be derived. However, when there are several competing reactions, fully mechanistic models contain a large number of parameters, making it difficult to obtain reliable estimates, so simplified models are necessary. We present a two-step approach to developing semi-mechanistic models of such processes. The first step involves the estimation of the selectivities of the enzyme, using profiles for the reaction species plotted against the degree of reaction, while the second step involves empirical fitting to the same data, but plotted as a function of time. We demonstrate this two-step approach through four case studies based on the literature data for the lipase-catalyzed esterification of fatty acids with trimethylolpropane to produce biolubricants. The semi-mechanistic models were able to describe the data well. Our approach has the advantage of allowing selectivities to be estimated without confounding effects from phenomena such as enzyme denaturation and inhibition. It therefore provides a promising framework for developing models of enzyme-catalyzed processes that obey Ping Pong bi bi kinetics.