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Sergei Grudinin
Person information
- affiliation: University Grenoble Alpes, Grenoble INP, France
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2020 – today
- 2024
- [b1]Sergei Grudinin:
New Challenges in Structural Bioinformatics: When Physics Meets Big Data. Grenoble Alpes University, France, 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) - 2022
- [c1]Dmitrii Zhemchuzhnikov, Ilia Igashov, Sergei Grudinin:
6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing. AAAI 2022: 4707-4715 - 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) - [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
- [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) - [i2]Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin:
Spherical convolutions on molecular graphs for protein model quality assessment. CoRR abs/2011.07980 (2020)
2010 – 2019
- 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) - [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) - 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)
Coauthor Index
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last updated on 2024-08-27 22:55 CEST by the dblp team
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