Although machine learning has a long-standing history in chemical research with respect to the prediction of molecular properties and biological activities, the quantitative modeling of reactivity has only been approached recently, and current models suggest that complex and specific parameterization is inevitable. As opposed to this, we report a simple machine learning model for predicting various reaction outcomes, such as yields and stereoselectivities. Being based on a solely structural input, our model should be transferable to diverse problems related to organic molecules.

Chemists go ML! This tutorial review provides easy access to the fundamentals of machine learning from a synthetic chemist's perspective. Its diverse applications for molecular design, synthesis planning, or reactivity prediction are summarized.

Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of "negative" examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitationsand demonstrate perspectives towards a long-term data quality enhancement in chemistry.

A deaminative strategy for the borylation of aliphatic primary amines is described. Alkyl radicals derived from the single‐electron reduction of redox‐active pyridinium salts, which can be isolated or generated in situ, were borylated in a visible light‐mediated reaction with bis(catecholato)diboron. No catalyst or further additives were required. The key electron donor–acceptor complex was characterized in detail by both experimental and computational investigations. The synthetic potential of this mild protocol was demonstrated through the late‐stage functionalization of natural products and drug molecules.

Herein, we present a novel strategy for the utilization of simple carbonyl compounds, aldehydes and ketones, as intermolecular radical acceptors. The reaction is enabled by visible light photoredox initiated hole catalysis and the in situ Brønsted acid activation of the carbonyl compound. This regioselective alkyl radical addition reaction does not require metals, ligands or additives and proceeds with a high degree of atom economy under mild conditions. The proposed mechanism is supported by both experimental and theoretical studies.