Method B 692 3.7. Selective Trifluoromethylation of Multi-Functional Substrates 693 3.8.
Enantioselective Trifluoromethylation 695 3.8. 1. Aldehydes and Ketones: With Cinchona …

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO
orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively …

We propose a machine-learning model, based on the random-forest method, to predict CO
adsorption in thiolate protected nanoclusters. Two phases of feature selection and training …

Physico–chemical characterization of nanoparticles in the context of their transport and fate
in the environment is an important challenge for risk assessment of nanomaterials. One of …

Reactivity scales are useful research tools for chemists, both experimental and
computational. However, to determine the reactivity of a single molecule, multiple …

The concept of nucleophilicity is at the basis of most transformations in chemistry.
Understanding and predicting the relative reactivity of different nucleophiles is therefore of …

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past
decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and …

Nucleophilicity provides important information about the chemical reactivity of organic
molecules. Experimental determination of the nucleophilicity parameter is a tedious and …

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity
for drug design. After binding to a biologically relevant target system, covalent compounds …

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's
approach to quantifying chemical reactivity has greatly facilitated the planning process, but …