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Found 2 papers, 2 papers with code
Molecular geometry prediction using a deep generative graph neural network
Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature.
Conditional molecular design with deep generative models
Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently.
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