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ORCIDSergei Grudinin
Person information
- affiliation: University Grenoble Alpes, Grenoble INP, France
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Books and Theses
- 2024
[b1]Sergei Grudinin:
New Challenges in Structural Bioinformatics: When Physics Meets Big Data. Grenoble Alpes University, France, 2024
Journal Articles
- 2021
- [j25]Maria Kadukova
, Karina dos Santos Machado, Pablo Chacón, Sergei Grudinin:
KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions. Bioinform. 37(7): 943-950 (2021) - [j24]Ilia Igashov, Kliment Olechnovic, Maria Kadukova, Ceslovas Venclovas, Sergei Grudinin:
VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures. Bioinform. 37(16): 2332-2339 (2021) - [j23]Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin:
Spherical convolutions on molecular graphs for protein model quality assessment. Mach. Learn. Sci. Technol. 2(4): 45005 (2021) - 2020
- [j22]Maria Kadukova, Vladimir Chupin, Sergei Grudinin:
Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J. Comput. Aided Mol. Des. 34(2): 191-200 (2020) - [j21]Andreas Haahr Larsen, Yong Wang, Sandro Bottaro, Sergei Grudinin, Lise Arleth, Kresten Lindorff-Larsen:
Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution. PLoS Comput. Biol. 16(4) (2020) - 2019
- [j20]Mikhail Karasikov, Guillaume Pagès, Sergei Grudinin:
Smooth orientation-dependent scoring function for coarse-grained protein quality assessment. Bioinform. 35(16): 2801-2808 (2019) - [j19]Guillaume Pagès, Benoit Charmettant, Sergei Grudinin:
Protein model quality assessment using 3D oriented convolutional neural networks. Bioinform. 35(18): 3313-3319 (2019) - [j18]Guillaume Pagès, Sergei Grudinin:
DeepSymmetry: using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures. Bioinform. 35(24): 5113-5120 (2019) - [j17]Petr Popov, Sergei Grudinin, Andrii Kurdiuk, Pavel Buslaev, Stephane Redon:
Controlled-advancement rigid-body optimization of nanosystems. J. Comput. Chem. 40(27): 2391-2399 (2019) - 2018
- [j16]Émilie Neveu, Petr Popov, Alexandre Hoffmann, Angelo Migliosi, Xavier Besseron, Grégoire Danoy, Pascal Bouvry, Sergei Grudinin:
RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules. Bioinform. 34(16): 2757-2765 (2018) - [j15]Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio, Guillaume Lamoureux:
Deep convolutional networks for quality assessment of protein folds. Bioinform. 34(23): 4046-4053 (2018) - [j14]Maria Kadukova, Sergei Grudinin:
Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2. J. Comput. Aided Mol. Des. 32(1): 151-162 (2018) - 2017
- [j13]Maria Kadukova, Sergei Grudinin:
Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization. J. Comput. Aided Mol. Des. 31(10): 943-958 (2017) - 2016
- [j12]Émilie Neveu, David W. Ritchie, Petr Popov, Sergei Grudinin:
PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation. Bioinform. 32(17): 693-701 (2016) - [j11]Sergei Grudinin, Maria Kadukova, Andreas Eisenbarth, Simon Marillet, Frédéric Cazals:
Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. J. Comput. Aided Mol. Des. 30(9): 791-804 (2016) - [j10]Sergei Grudinin, Petr Popov, Émilie Neveu, Georgy Cheremovskiy:
Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential. J. Chem. Inf. Model. 56(6): 1053-1062 (2016) - [j9]Maria Kadukova, Sergei Grudinin:
Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates. J. Chem. Inf. Model. 56(8): 1410-1419 (2016) - 2015
- [j8]Petr Popov, Sergei Grudinin:
Knowledge of Native Protein-Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates. J. Chem. Inf. Model. 55(10): 2242-2255 (2015) - 2014
- [j7]Petr Popov, Sergei Grudinin:
Rapid determination of RMSDs corresponding to macromolecular rigid body motions. J. Comput. Chem. 35(12): 950-956 (2014) - 2013
- [j6]Maël Bosson, Sergei Grudinin, Stephane Redon:
Block-adaptive quantum mechanics: An adaptive divide-and-conquer approach to interactive quantum chemistry. J. Comput. Chem. 34(6): 492-504 (2013) - 2012
- [j5]Maël Bosson, Caroline Richard, Antoine Plet, Sergei Grudinin, Stephane Redon:
Interactive quantum chemistry: A divide-and-conquer ASED-MO method. J. Comput. Chem. 33(7): 779-790 (2012) - [j4]Maël Bosson, Sergei Grudinin, Xavier Bouju, Stephane Redon:
Interactive physically-based structural modeling of hydrocarbon systems. J. Comput. Phys. 231(6): 2581-2598 (2012) - 2011
- [j3]Svetlana Artemova, Sergei Grudinin, Stephane Redon:
Fast construction of assembly trees for molecular graphs. J. Comput. Chem. 32(8): 1589-1598 (2011) - [j2]Svetlana Artemova, Sergei Grudinin, Stephane Redon:
A comparison of neighbor search algorithms for large rigid molecules. J. Comput. Chem. 32(13): 2865-2877 (2011) - 2010
- [j1]Sergei Grudinin, Stephane Redon:
Practical modeling of molecular systems with symmetries. J. Comput. Chem. 31(9): 1799-1814 (2010)
Conference and Workshop Papers
- 2024
- [c2]Dmitrii Zhemchuzhnikov, Sergei Grudinin:
ILPO-NET: Network for the Invariant Recognition of Arbitrary Volumetric Patterns in 3D. ECML/PKDD (4) 2024: 352-368 - 2022
- [c1]Dmitrii Zhemchuzhnikov, Ilia Igashov, Sergei Grudinin:
6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing. AAAI 2022: 4707-4715
Informal and Other Publications
- 2024
- [i6]Dmitrii Zhemchuzhnikov, Sergei Grudinin:
ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D. CoRR abs/2403.19612 (2024) - [i5]Dmitrii Zhemchuzhnikov, Sergei Grudinin:
On the Fourier analysis in the SO(3) space : EquiLoPO Network. CoRR abs/2404.15979 (2024) - 2021
- [i4]Elodie Laine, Stephan Eismann, Arne Elofsson, Sergei Grudinin:
Protein sequence-to-structure learning: Is this the end(-to-end revolution)? CoRR abs/2105.07407 (2021) - [i3]Dmitrii Zhemchuzhnikov, Ilia Igashov, Sergei Grudinin:
6DCNN with roto-translational convolution filters for volumetric data processing. CoRR abs/2107.12078 (2021) - 2020
- [i2]Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin:
Spherical convolutions on molecular graphs for protein model quality assessment. CoRR abs/2011.07980 (2020) - 2018
- [i1]Guillaume Pagès, Sergei Grudinin:
DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures. CoRR abs/1810.12026 (2018)
Coauthor Index
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