1 citations · 2 across the 6 of their papers we have counts for
6 papers
HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration
Daria A. Ryabchenko, Pavel Gurevich, Shamil Kadyrov +5
Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, phy…
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Daria Frolova, Talgat Daulbaev, Egor Sevriugov +4
Predicting a protein--ligand binding pose is central to structure-based drug design, yet current docking methods still struggle to combine speed, accuracy and physical plausibility…
Redesigning Out-of-Distribution Detection on 3D Medical Images
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
Detecting out-of-distribution (OOD) samples for trusted medical image segmentation remains a significant challenge. The critical issue here is the lack of a strict definition of ab…
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
Deep Learning models perform unreliably when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distributi…
Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images
Daria Frolova, Anton Vasiliuk, Mikhail Belyaev +1
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-d…
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation
Anton Vasiliuk, Daria Frolova, Mikhail Belyaev +1
When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could…