
pmid: 31190012
Organic chemistry has largely been conducted in an ad hoc manner by academic laboratories that are funded by grants directed towards the investigation of specific goals or hypotheses. Although modern synthetic methods can provide access to molecules of considerable complexity, predicting the outcome of a single chemical reaction remains a major challenge. Improvements in the prediction of 'above-the-arrow' reaction conditions are needed to enable intelligent decision making to select an optimal synthetic sequence that is guided by metrics including efficiency, quality and yield. Methods for the communication and the sharing of data will need to evolve from traditional tools to machine-readable formats and open collaborative frameworks. This will accelerate innovation and require the creation of a chemistry commons with standardized data handling, curation and metrics.
Machine Learning, Halogenation, Information Dissemination, Open Access Publishing, Chemistry, Pharmaceutical, Chemistry Techniques, Synthetic, Diffusion of Innovation, Diterpenes, Decision Making, Computer-Assisted
Machine Learning, Halogenation, Information Dissemination, Open Access Publishing, Chemistry, Pharmaceutical, Chemistry Techniques, Synthetic, Diffusion of Innovation, Diterpenes, Decision Making, Computer-Assisted
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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