Search Results for author:
Found 5 papers, 4 papers with code
Low-count Time Series Anomaly Detection
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types.
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials.
Ranked #26 on
Single-step retrosynthesis
on USPTO-50k
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
We propose a novel distance measure between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that can be used as an evaluation metric for generative models.
