Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors

Distribution and mobility of heavy elements in floodplain agricultural soils along the Ibar River (Southern Serbia and Northern Kosovo). Chemometric investigation of pollutant sources and ecological risk assessment

This work investigates the influence of a high-magnitude flood event on heavy elements (HEs) pollution and mobility in the agricultural soils along Ibar River in Southern Serbia and Northern Kosovo. The study area was one of the most important Pb/Zn industrial regions in Europe. Soil samples (n = 50) collected before and after the floods in May 2014 were subjected to the sequential extraction procedure proposed by the Community Bureau of Reference (BCR). The results indicated that the floods significantly increased not only the pseudo total concentrations of HEs in the soil but also their mobile and potentially bioavailable amounts. Moreover, higher concentrations (both pseudo total and potentially bioavailable) were found in the agricultural soils closer to the industrial hotspots. Principal component analysis and hierarchical cluster analysis successfully grouped the analyzed elements according to their anthropogenic or natural origin. The floods significantly increased the potential ecological risk of HEs associated with Pb/Zn industrial activities in the study area. The potential ecological risk of Cd after the floods was highest and should be of special concern