Michal Brylinski
Orcid: 0000-0002-6204-2869
According to our database1,
Michal Brylinski
authored at least 35 papers
between 2004 and 2024.
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Bibliography
2024
Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet.
Mach. Learn. Knowl. Extr., September, 2024
2022
Proceedings of the 14th International Conference on COMmunication Systems & NETworkS, 2022
2021
GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data.
J. Cheminformatics, 2021
2020
BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.
Bioinform., 2020
2019
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.
PLoS Comput. Biol., 2019
J. Comput. Aided Mol. Des., 2019
Briefings Bioinform., 2019
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019
2018
Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.
BMC Bioinform., 2018
The CSBG - LSU Gateway: Web based Hosted Gateway for Computational System Biology Application Tools from Louisiana State University.
Proceedings of the Practice and Experience on Advanced Research Computing, 2018
2017
Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with <i>e</i>MolFrag.
J. Chem. Inf. Model., 2017
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks.
BMC Bioinform., 2017
2016
J. Cheminformatics, 2016
Assessing the similarity of ligand binding conformations with the Contact Mode Score.
Comput. Biol. Chem., 2016
PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.
Bioinform., 2016
2015
Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.
J. Cheminformatics, 2015
GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field.
J. Comput. Chem., 2015
Briefings Bioinform., 2015
Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?
Bio Algorithms Med Syst., 2015
2014
<i>e</i>MatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models.
PLoS Comput. Biol., 2014
2013
Nonlinear Scoring Functions for Similarity-Based Ligand Docking and Binding Affinity Prediction.
J. Chem. Inf. Model., 2013
eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.
J. Comput. Aided Mol. Des., 2013
2010
Comprehensive Structural and Functional Characterization of the Human Kinome by Protein Structure Modeling and Ligand Virtual Screening.
J. Chem. Inf. Model., 2010
J. Comput. Chem., 2010
Bioinform., 2010
2009
PLoS Comput. Biol., 2009
FINDSITE: a combined evolution/structure-based approach to protein function prediction.
Briefings Bioinform., 2009
2008
Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints.
J. Comput. Chem., 2008
2007
PLoS Comput. Biol., 2007
Int. J. Bioinform. Res. Appl., 2007
2006
Comput. Biol. Chem., 2006
2004
Bioinform., 2004