Limited conformational space for early-stage protein folding simulation. - Michal Brylinski; Wiktor Jurkowski; Leszek Konieczny; Irena Roterman (Bioinform., 2004)

SPI - Structure predictability index for protein sequences. - Michal Brylinski; Leszek Konieczny; Irena Roterman (Silico Biol., 2004)

Hydrophobic collapse in (in silico) protein folding. - Michal Brylinski; Leszek Konieczny; Irena Roterman (Comput. Biol. Chem., 2006)

Sequence-Structure-Function Relation Characterized in silico. - Michal Brylinski; Marek Kochanczyk; Leszek Konieczny; Irena Roterman (Silico Biol., 2006)

Gauss-Function-Based Model of Hydrophobicity Density in Proteins. - Leszek Konieczny; Michal Brylinski; Irena Roterman (Silico Biol., 2006)

Is the protein folding an aim-oriented process? Human haemoglobin as example. - Michal Brylinski; Leszek Konieczny; Irena Roterman (Int. J. Bioinform. Res. Appl., 2007)

Prediction of Functional Sites Based on the Fuzzy Oil Drop Model. - Michal Brylinski; Katarzyna Prymula; Wiktor Jurkowski; Marek Kochanczyk; Ewa Stawowczyk; Leszek Konieczny; Irena Roterman (PLoS Comput. Biol., 2007)

Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints. - Michal Brylinski; Jeffrey Skolnick (J. Comput. Chem., 2008)

FINDSITE: a combined evolution/structure-based approach to protein function prediction. - Jeffrey Skolnick; Michal Brylinski (Briefings Bioinform., 2009)

FINDSITELHM: A Threading-Based Approach to Ligand Homology Modeling. - Michal Brylinski; Jeffrey Skolnick (PLoS Comput. Biol., 2009)

PSiFR: an integrated resource for prediction of protein structure and function. - Shashi Bhushan Pandit; Michal Brylinski; Hongyi Zhou; Mu Gao; Adrian K. Arakaki; Jeffrey Skolnick (Bioinform., 2010)

Q-DockLHM: Low-resolution refinement for ligand comparative modeling. - Michal Brylinski; Jeffrey Skolnick (J. Comput. Chem., 2010)

Comprehensive Structural and Functional Characterization of the Human Kinome by Protein Structure Modeling and Ligand Virtual Screening. - Michal Brylinski; Jeffrey Skolnick (J. Chem. Inf. Model., 2010)

eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. - Michal Brylinski; Wei Pan Feinstein (J. Comput. Aided Mol. Des., 2013)

Nonlinear Scoring Functions for Similarity-Based Ligand Docking and Binding Affinity Prediction. - Michal Brylinski (J. Chem. Inf. Model., 2013)

eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models. - Michal Brylinski (PLoS Comput. Biol., 2014)

Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? - Michal Brylinski (Bio Algorithms Med Syst., 2015)

Predicting protein interface residues using easily accessible on-line resources. - Surabhi Maheshwari; Michal Brylinski (Briefings Bioinform., 2015)

GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. - Yun Ding; Ye Fang; Wei Pan Feinstein; Jagannathan Ramanujam; David M. Koppelman; Juana Moreno; Michal Brylinski; Mark Jarrell (J. Comput. Chem., 2015)

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. - Wei Pan Feinstein; Michal Brylinski (J. Cheminformatics, 2015)

PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. - Chen Wang; Gang Hu; Kui Wang; Michal Brylinski; Lei Xie; Lukasz A. Kurgan (Bioinform., 2016)

Assessing the similarity of ligand binding conformations with the Contact Mode Score. - Yun Ding; Ye Fang; Juana Moreno; J. Ramanujam; Mark Jarrell; Michal Brylinski (Comput. Biol. Chem., 2016)

A graph-based approach to construct target-focused libraries for virtual screening. - Misagh Naderi; Chris Alvin; Yun Ding; Supratik Mukhopadhyay; Michal Brylinski (J. Cheminformatics, 2016)

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. - Surabhi Maheshwari; Michal Brylinski (BMC Bioinform., 2017)

Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag. - Tairan Liu; Misagh Naderi; Chris Alvin; Supratik Mukhopadhyay; Michal Brylinski (J. Chem. Inf. Model., 2017)

The CSBG - LSU Gateway: Web based Hosted Gateway for Computational System Biology Application Tools from Louisiana State University. - Eroma Abeysinghe; Michal Brylinski; Marcus Christie; Suresh Marru; Marlon E. Pierce (PEARC, 2018)

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. - Rajiv Gandhi Govindaraj; Michal Brylinski (BMC Bioinform., 2018)

LSU Computational System Biology Gateway for Education. - Eroma Abeysinghe; Michal Brylinski; Marcus Christie; Suresh Marru; Marlon E. Pierce (PEARC, 2019)

Binding site matching in rational drug design: algorithms and applications. - Misagh Naderi; Jeffrey Mitchell Lemoine; Rajiv Gandhi Govindaraj; Omar Kana; Wei Pan Feinstein; Michal Brylinski (Briefings Bioinform., 2019)

Elucidating the druggability of the human proteome with eFindSite. - Omar Kana; Michal Brylinski (J. Comput. Aided Mol. Des., 2019)

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. - Limeng Pu; Rajiv Gandhi Govindaraj; Jeffrey Mitchell Lemoine; Hsiao-Chun Wu; Michal Brylinski (PLoS Comput. Biol., 2019)

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network. - Wentao Shi; Jeffrey Mitchell Lemoine; Abd-El-Monsif A. Shawky; Manali Singha; Limeng Pu; Shuangyan Yang; J. Ramanujam; Michal Brylinski (Bioinform., 2020)

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data. - Guannan Liu; Manali Singha; Limeng Pu; Prasanga Neupane; Joseph Feinstein; Hsiao-Chun Wu; J. Ramanujam; Michal Brylinski (J. Cheminformatics, 2021)

DeepDrug: Applying AI for the Advancement of Drug Discovery. - Supratik Mukhopadhyay; Michal Brylinski; Adam Bess; Frej Berglind; Chris Galliano; Patrick F. McGrew (COMSNETS, 2022)

Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet. - Mengmeng Liu; Gopal N. Srivastava; J. Ramanujam; Michal Brylinski (Mach. Learn. Knowl. Extr., 2024)

EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations. - Mengmeng Liu; Xialong Ni; J. Ramanujam; Michal Brylinski (J. Chem. Inf. Model., 2025)