101 citations · 152 across the 6 of their papers we have counts for
8 papers
Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing +3
Fragment-based drug design is a promising strategy leveraging the binding of small chemical moieties that can efficiently guide drug discovery. The initial step of fragment identif…
Multi-domain Distribution Learning for De Novo Drug Design
Arne Schneuing, Ilia Igashov, Adrian W. Dobbelstein +3
We introduce DrugFlow, a generative model for structure-based drug design that integrates continuous flow matching with discrete Markov bridges, demonstrating state-of-the-art perf…
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
Miruna Cretu, Charles Harris, Ilia Igashov +6
Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically…
RetroBridge: Modeling Retrosynthesis with Markov Bridges
Ilia Igashov, Arne Schneuing, Marwin Segler +2
Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule. Each…
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Ilia Igashov, Hannes Stärk, Clément Vignac +5
Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fra…
Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing, Charles Harris, Yuanqi Du +10
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods lev…
6DCNN with roto-translational convolution filters for volumetric data processing
Dmitrii Zhemchuzhnikov, Ilia Igashov, Sergei Grudinin
In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing…
Spherical convolutions on molecular graphs for protein model quality assessment
Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin
Processing information on 3D objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neu…