Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

• Stefan P. Schmid,
• Leon Schlosser,
• Frank Glorius and
• Kjell Jorner

Beilstein J. Org. Chem. 2024, 20, 2280–2304, doi:10.3762/bjoc.20.196

• Drawing inspiration from linear free energy relationships, MLR models, pioneered by Norrby and co-workers [83] and later further developed by Sigman and co-workers [69][82], are commonly used for the prediction of enantioselectivity. In such models, the substrates, catalysts, and other relevant reaction

• by observing the influence of substituents on a reaction outcome. However, Hammett parameters have shown to not fully describe observed trends. Therefore, complementary representations capturing other properties of a molecule have been derived (vide infra) [28]. While traditional linear free energy

• relationships such as those using Hammett parameters used linear models, the emergence of ML has led to the development of more complex algorithms, better suited for extracting hidden patterns in data. The ability of ML to efficiently capture complex relationships allows to extract influences on catalyst

Phosphodiester models for cleavage of nucleic acids

• Satu Mikkola,
• Tuomas Lönnberg and
• Harri Lönnberg

Beilstein J. Org. Chem. 2018, 14, 803–837, doi:10.3762/bjoc.14.68

• artificial cleaving agents that have enzyme-like catalytic properties but are more robust. pH-Rate profiles, linear free energy relationships and kinetic heavy atom isotope effects are the experimental approaches that are, together with construction of multifunctional cleaving agents, most extensively used

• obtained [29][32]. Linear free energy relationships are, in turn, used to determine the position of transition state on the reaction coordinate [33]. The polar property of either entering or departing nucleophile or non-departing groups is altered in a systematic manner and the effect on reaction rate is

On the mechanism of action of gated molecular baskets: The synchronicity of the revolving motion of gates and in/out trafficking of guests

• Keith Hermann,
• Stephen Rieth,
• Hashem A. Taha,
• Bao-Yu Wang,
• Christopher M. Hadad and
• Jovica D. Badjić

Beilstein J. Org. Chem. 2012, 8, 90–99, doi:10.3762/bjoc.8.9

• the basket: Specifically, the greater the affinity of the guest for occupying the basket, the less effective the gates are in “sweeping” the guest as the gates undergo their revolving motion. Keywords: dynamic NMR; host–guest chemistry; linear free-energy relationships; molecular encapsulation

Equilibrium constants and protonation site for N-methylbenzenesulfonamides

• José A. Moreira,
• Ana M. Rosa da Costa,
• Luis García-Río and
• Márcia Pessêgo

Beilstein J. Org. Chem. 2011, 7, 1732–1738, doi:10.3762/bjoc.7.203

• parameter than with σ, which indicates that the initial protonation site is the oxygen atom of the sulfonyl group. Keywords: linear free-energy relationships; N-methylbenzenesulfonamides; protonation equilibrium; Introduction Having a knowledge of the protonation equilibrium constants for N

Alkene selenenylation: A comprehensive analysis of relative reactivities, stereochemistry and asymmetric induction, and their comparisons with sulfenylation

• Vadim A. Soloshonok and
• Donna J. Nelson

Beilstein J. Org. Chem. 2011, 7, 744–758, doi:10.3762/bjoc.7.85

• magnitude greater (Table 1, entries 20 versus 21, or 26 versus 27). 3. Alkyl branching α to C=C: The data obtained in this study pointed to a new steric effect in linear free energy relationships, which can be defined as alkyl group α-branching. Thus, within each line fit, there is a clear trend dependent