Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey-Bakshi-Shibata Reduction.

We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔ of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee).

Baseline metal and metalloid contamination in two marine sponge species, Hymeniacidon heliophila and Desmapsamma anchorata, from southeastern Brazil.

Sponges are not routinely employed as metal bioindicators in Brazil. In this sense, this study reports baseline metal and metalloid concentrations, determined by inductively coupled plasma mass spectrometry, for two Demospongiae sponge species, Hymeniacidon heliophila and Desmapsamma anchorata, sampled from two Southeastern Brazil areas. Sponges from Ilha Grande Bay, an Environmental Protection Area, exhibited higher Al, As, Cd, Co, Cr, Fe, and Ni levels compared to Vermelha Beach, a metropolitan area in the Rio de Janeiro city.