Driven by legislation and evolving attitudes towards environmental issues, establishing green solvents for extractions, separations, formulations and reaction chemistry has become an increasingly important area of research. Several general purpose solvent selection guides have now been published with the aim to reduce use of the most hazardous solvents. This review serves the purpose of explaining the role of these guides, highlighting their similarities and differences. How they can be used most effectively to enhance the greenness of chemical processes, particularly in laboratory organic synthesis and the pharmaceutical industry, is addressed in detail.

A deep learning approach coupling the Tree-LSTM network and back-propagation neural network for predicting the octanol–water partition coefficient.

Deep learning rapidly promotes many fields with successful stories in natural language processing. An architecture of deep neural network (DNN) combining tree‐structured long short‐term memory (Tree‐LSTM) network and back‐propagation neural network (BPNN) is developed for predicting physical properties. Inspired by the natural language processing in artificial intelligence, we first developed a strategy for data preparation including encoding molecules with canonical molecular signatures and vectorizing bond‐substrings by an embedding algorithm. Then, the dynamic neural network named Tree‐LSTM is employed to depict molecular tree data‐structures while the BPNN is used to correlate properties. To evaluate the performance of proposed DNN, the critical properties of nearly 1,800 compounds are employed for training and testing the DNN models. As compared with classical group contribution methods, it can be demonstrated that the learned DNN models are able to provide more accurate prediction and cover more diverse molecular structures without considering frequencies of substructures.

Dimethyl carbonate (DMC) chemistry has been extended to include acid-catalysed reactions of different aliphatic alcohols and phenols.

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