Systematic development of vanadium catalysts for sustainable epoxidation of small alkenes and allylic alcohols

The catalytic epoxidation of small alkenes and allylic alcohols includes a wide range of valuable chemical applications, with many works describing vanadium complexes as suitable catalysts towards sustainable process chemistry. But, given the complexity of these mechanisms, it is not always easy to sort out efficient examples for streamlining sustainable processes and tuning product optimization. In this review, we provide an update on major works of tunable vanadium-catalyzed epoxidations, with a focus on sustainable optimization routes. After presenting the current mechanistic view on vanadium catalysts for small alkenes and allylic alcohols’ epoxidation, we argue the key challenges in green process development by highlighting the value of updated kinetic and mechanistic studies, along with essential computational studies.The authors thank the Fundação para a Ciência e Tecnologia (FCT/MCTES) support to LAQV-REQUIMTE (UIDP/50006/2020). José Ferraz-Caetano’s PhD Fellowship is supported by a doctoral Grant (SFRH/BD/151159/2021) financed by the FCT, with funds from the Portuguese State and EU Budget, through the Social European Fund and Programa Por_Centro, under the MIT Portugal Program

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug–Enzyme Interactions

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug–enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software