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Found 9 papers, 5 papers with code
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery.
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites.
Local Latent Space Bayesian Optimization over Structured Inputs
By reformulating the encoder to function as both an encoder for the DAE globally and as a deep kernel for the surrogate model within a trust region, we better align the notion of local optimization in the latent space with local optimization in the input space.
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it.
A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.
Generating Molecules via Chemical Reactions
We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.
A Generative Model For Electron Paths
Chemical reactions can be described as the stepwise redistribution of electrons in molecules.
Are Generative Classifiers More Robust to Adversarial Attacks?
There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed.
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift.
