Ilia Igashov

According to our database, Ilia Igashov authored at least 12 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Multi-domain Distribution Learning for De Novo Drug Design.

Arne Schneuing, Ilia Igashov, Adrian W. Dobbelstein, Thomas Castiglione, Michael M. Bronstein, Bruno Correia

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints.

Miruna T. Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Bruno E. Correia, Julien Roy, Emmanuel Bengio, Pietro Lio

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Structure-based drug design with equivariant diffusion models.

Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Rokkum Jamasb, Ilia Igashov, Weitao Du, Carla P. Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael M. Bronstein, Bruno E. Correia

Nat. Comput. Sci., December, 2024

Equivariant 3D-conditional diffusion model for molecular linker design.

Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia

Nat. Mac. Intell., 2024

RetroBridge: Modeling Retrosynthesis with Markov Bridges.

Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

The Second Learning on Graphs Conference: Preface.

Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

2022

Structure-based Drug Design with Equivariant Diffusion Models.

Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael M. Bronstein, Bruno E. Correia

CoRR, 2022

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design.

Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia

CoRR, 2022

The First Learning on Graphs Conference: Preface.

Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov

Proceedings of the Learning on Graphs Conference, 2022

6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing.

Dmitrii Zhemchuzhnikov, Ilia Igashov, Sergei Grudinin

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Spherical convolutions on molecular graphs for protein model quality assessment.

Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin

Mach. Learn. Sci. Technol., 2021

VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures.

Ilia Igashov, Kliment Olechnovic, Maria Kadukova, Ceslovas Venclovas, Sergei Grudinin

Bioinform., 2021