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Found 5 papers, 4 papers with code
Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted.
A generalized-template-based graph neural network for accurate organic reactivity prediction
In addition to the built-in interpretability of the generalized reaction templates, the high score–accuracy correlation of the model allows users to assess the uncertainty of the machine predictions.
Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
Our model shows a promising 89. 5 and 99. 2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions.
Ranked #8 on
Single-step retrosynthesis
on USPTO-50k
Explaining How Deep Neural Networks Forget by Deep Visualization
Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life.
Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization
Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human life.
