1 citations · 1 across the 2 of their papers we have counts for
3 papers
Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles
Jie Li, Kathryn E. Kirchoff, Dante A. Pertusi +1
Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the e…
Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search
Kathryn E. Kirchoff, James Wellnitz, Joshua E. Hochuli +4
Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task st…
SALSA: Semantically-Aware Latent Space Autoencoder
Kathryn E. Kirchoff, Travis Maxfield, Alexander Tropsha +1
In deep learning for drug discovery, chemical data are often represented as simplified molecular-input line-entry system (SMILES) sequences which allow for straightforward implemen…