licheng-xu-echo/SyntheticSpacePrediction
This is a repository for paper "Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning"
This project helps synthetic chemists predict the enantioselectivity of asymmetric palladium-catalyzed electro-oxidative C–H bond activation reactions. By inputting reaction components, it leverages machine learning with transition state knowledge to output quantitative predictions across a vast synthetic space. The end-user is a synthetic chemist aiming to discover and optimize new asymmetric reactions more efficiently.
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Use this if you need to quantitatively evaluate the enantioselectivity of a wide range of potential asymmetric catalysis reactions without exhaustive lab work.
Not ideal if your primary goal is to perform general machine learning tasks or if you are not working with asymmetric catalysis in synthetic chemistry.
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MIT
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Feb 08, 2024
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