The influence of negative training set size on machine learning-based virtual screening R Kurczab, S Smusz, AJ Bojarski Journal of cheminformatics 6, 1-9, 2014 | 83 | 2014 |
The influence of the inactives subset generation on the performance of machine learning methods S Smusz, R Kurczab, AJ Bojarski Journal of cheminformatics 5, 1-8, 2013 | 45 | 2013 |
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 | 39 | 2013 |
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 | 29 | 2015 |
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 | 27 | 2014 |
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 | 24 | 2015 |
Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning MethodsA 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 | 14 | 2015 |
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, 1-9, 2015 | 13 | 2015 |
Evaluation of different machine learning methods for ligand-based virtual screening R Kurczab, S Smusz, AJ Bojarski Journal of Cheminformatics 3, 1-1, 2011 | 12 | 2011 |
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 | 9 | 2015 |
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, 1-1, 2013 | 3 | 2013 |
The influence of training actives/inactives ratio on machine learning performance R Kurczab, S Smusz, AJ Bojarski Journal of Cheminformatics 5, 1-1, 2013 | 3 | 2013 |
The influence of hashed fingerprints density on the machine learning methods performance S Smusz, R Kurczab, AJ Bojarski Journal of Cheminformatics 5, 1-1, 2013 | 2 | 2013 |
Ocenianie w szkole–trud i odpowiedzialność K Dudek, A Kopytek, MA Płotek, S Smusz Zeszyty Naukowe Towarzystwa Doktorantów Uniwersytetu Jagiellońskiego. Nauki …, 2013 | 2 | 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 |
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 | | |
UNCERTAINTY OF THE IN VITRO EXPERIMENTS IN THE CONSTRUCTION OF PREDICTIVE MODELS S Smusz, W Czarnecki, D Warszycki, AJ Bojarski | | |
Studies of hashed fingerprint density in terms of its influence on machine learning methods performance S Smusz, R Kurczab, AJ Bojarski | | |
Application of Machine Learning to Structural Interaction Fingerprints insight into activity and selectivity of ligands– J Witek, S Smusz, K Rataj, S Mordalski, AJ Bojarski | | |