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Embedding Molecules into a Latent Space
As mentioned in my previous post on predicting bee toxicity, it is often necessary to convert heterogeneous molecular structures into latent-space representations to allow machine learning methods to identify predictive features. After publishing the bee toxicity paper, a potential collaborator (hi Vasuk!) sent over a data set, asking if we might be able to apply a similar method to extract meaningful structure-activity relationships.
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Graph Kernels and SVM to Predict Bee Toxicity
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PoreMatMod.jl: Chemical Substructure Find/Replace
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