Search Results for author:
Found 7 papers, 3 papers with code
Predicting emergence of crystals from amorphous matter with deep learning
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.
A principal odor map unifies diverse tasks in olfactory perception
Mapping molecular structure to odor perception is a key challenge in olfaction.
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule.
Automatic chemical design using a data-driven continuous representation of molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.
Neural networks for the prediction organic chemistry reactions
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning.
