K
Kristina Preuer
Researcher at Johannes Kepler University of Linz
Publications - 6
Citations - 644
Kristina Preuer is an academic researcher from Johannes Kepler University of Linz. The author has contributed to research in topics: Metric (mathematics) & Deep learning. The author has an hindex of 5, co-authored 6 publications receiving 372 citations.
Papers
More filters
Journal ArticleDOI
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
Kristina Preuer,Richard J. Lewis,Sepp Hochreiter,Andreas Bender,Krishna C. Bulusu,Krishna C. Bulusu,Günter Klambauer +6 more
TL;DR: DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies and could be a valuable tool for selecting novel synergistic drug combinations.
Journal ArticleDOI
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
TL;DR: An evaluation metric for generative models called Fréchet ChemNet distance (FCD) is proposed that can detect whether generated molecules are diverse and have similar chemical and biological properties as real molecules.
Book ChapterDOI
Interpretable Deep Learning in Drug Discovery
TL;DR: In this paper, the authors show how single neurons can be interpreted as classifiers which determine the presence or absence of pharmacophore or toxicophore-like structures, thereby generating new insights and relevant knowledge for chemistry, pharmacology and biochemistry.
Posted Content
Interpretable Deep Learning in Drug Discovery
TL;DR: It is shown how single neurons can be interpreted as classifiers which determine the presence or absence of pharmacophores- or toxicophore-like structures, thereby generating new insights and relevant knowledge for chemistry, pharmacology and biochemistry.
Posted Content
Fréchet ChemblNet Distance: A metric for generative models for molecules.
TL;DR: A novel distance measure between two sets of molecules, called Fr\'echet ChemblNet distance (FCD), that can be used as an evaluation metric for generative models and an easy-to-use implementation that only requires the SMILES representation of the generated molecules as input to calculate the FCD.