Sabina Smusz
Sabina Smusz
Verified email at if-pan.krakow.pl
TitleCited byYear
The influence of negative training set size on machine learning-based virtual screening
R Kurczab, S Smusz, AJ Bojarski
Journal of cheminformatics 6 (1), 32, 2014
332014
The influence of the inactives subset generation on the performance of machine learning methods
S Smusz, R Kurczab, AJ Bojarski
Journal of cheminformatics 5 (1), 17, 2013
272013
A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds
S Smusz, R Kurczab, AJ Bojarski
Chemometrics and Intelligent Laboratory Systems 128, 89-100, 2013
232013
An application of machine learning methods to structural interaction fingerprints—a case study of kinase inhibitors
J Witek, S Smusz, K Rataj, S Mordalski, AJ Bojarski
Bioorganic & medicinal chemistry letters 24 (2), 580-585, 2014
212014
Fingerprint-based consensus virtual screening towards structurally new 5-HT6R ligands
S Smusz, R Kurczab, G Satała, AJ Bojarski
Bioorganic & medicinal chemistry letters 25 (9), 1827-1830, 2015
142015
Imidazolidine-4-one derivatives in the search for novel chemosensitizers of Staphylococcus aureus MRSA: Synthesis, biological evaluation and molecular modeling studies
A Matys, S Podlewska, K Witek, J Witek, AJ Bojarski, J Schabikowski, ...
European journal of medicinal chemistry 101, 313-325, 2015
132015
Evaluation of different machine learning methods for ligand-based virtual screening
R Kurczab, S Smusz, AJ Bojarski
Journal of cheminformatics 3 (1), P41, 2011
82011
Multiple conformational states in retrospective virtual screening–homology models vs. crystal structures: beta-2 adrenergic receptor case study
S Mordalski, J Witek, S Smusz, K Rataj, AJ Bojarski
Journal of cheminformatics 7 (1), 13, 2015
72015
Exploiting uncertainty measures in compounds activity prediction using support vector machines
S Smusz, WM Czarnecki, D Warszycki, AJ Bojarski
Bioorganic & medicinal chemistry letters 25 (1), 100-105, 2015
72015
Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods A Case Study of Serotonin Receptors 5-HT6 and 5-HT7
S Smusz, S Mordalski, J Witek, K Rataj, R Kafel, AJ Bojarski
Journal of chemical information and modeling 55 (4), 823-832, 2015
62015
Application of Structural Interaction Fingerpints (SIFts) into post-docking analysis-insight into activity and selectivity
J Witek, K Rataj, S Mordalski, S Smusz, T Kosciolek, AJ Bojarski
Journal of cheminformatics 5 (S1), P28, 2013
22013
The influence of training actives/inactives ratio on machine learning performance
R Kurczab, S Smusz, AJ Bojarski
Journal of cheminformatics 5 (1), P30, 2013
22013
The influence of hashed fingerprints density on the machine learning methods performance
S Smusz, R Kurczab, AJ Bojarski
Journal of cheminformatics 5 (1), P25, 2013
12013
OCENIANIE W SZKOLE–TRUD I ODPOWIEDZIALNOŚĆ
K Dudek, A Kopytek, MA Płotek, S Smusz
Zeszyty Naukowe Towarzystwa Doktorantów Uniwersytetu Jagiellońskiego. Nauki …, 2013
2013
A novel machine learning-based protocol for predicting biological activity of chemical compounds
S Smusz, S Mordalski, J Witek, K Rataj, AJ Bojarski
GPCR Workshop, 01-05.12, 2013
2013
Automated docking restrains assignment based on interaction profiles
S Mordalski, J Witek, K Rataj, S Smusz, AJ Bojarski
GPCR Workshop, 01-05.12, 2013
2013
Poszukiwanie związków biologicznie aktywnych z wykorzystaniem metod uczenia maszynowego
S Smusz, R Kurczab, AJ Bojarski
Fundacja Rozwoju Nauki i Biznesu w Obszarze Nauk Medycznych i Ścisłych, 2012
2012
Hybridization of ligands as a way of generating combinatorial libraries of drug candidates
S Smusz, R Kurczab, D Warszycki, T Kościółek, S Mordalski, A Bojarski
Chem. Lett 8, 2465, 2010
2010
A machine learning-based protocol for docking results analysis
S Smusz, S Mordalski, J Witek, K Rataj, AJ Bojarski
An application of ligand interaction profiles as a novel approach in virtual screening of GPCR ligands
J Witek, S Smusz, K Rataj, S Mordalski, D Warszycki, AJ Bojarski
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Articles 1–20