PMID- 27160290
OWN - NLM
STAT- MEDLINE
DCOM- 20171003
LR  - 20191210
IS  - 1875-6220 (Electronic)
IS  - 1570-1638 (Linking)
VI  - 13
IP  - 2
DP  - 2016
TI  - Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and 
      Experimental Studies.
PG  - 109-19
AB  - Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids 
      (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 
      10231) have been studied. Predictive QSAR models were built using different 
      descriptor sets for a set of 88 ionic liquids with known minimum inhibitory 
      concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR 
      methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative 
      Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum 
      non-linear regression models. The obtained models were validated using a 5-fold 
      cross-validation approach and resulted in the prediction accuracies of 80% +/- 5.0 
      for the classification models and q2 = 0.73-0.87 for the non-linear regression 
      models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic 
      liquids with predicted activity was performed by disco-diffusion method against 
      C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei 
      and C. glabrata strains. The high percentage of coincidence between the QSAR 
      predictions and the experimental results confirmed the high predictive power of 
      the developed QSAR models within the applicability domain of new imidazolium 
      ionic liquids.
FAU - Hodyna, Diana
AU  - Hodyna D
AD  - Institute of Bioorganic Chemistry and Petrochemistry, National Academy of 
      Sciences of Ukraine, 1 Murmanska Street, Kyiv-94, 02660, Ukraine. 
      dianahodyna@gmail.com.
FAU - Kovalishyn, Vasyl
AU  - Kovalishyn V
FAU - Rogalsky, Sergiy
AU  - Rogalsky S
FAU - Blagodatnyi, Volodymyr
AU  - Blagodatnyi V
FAU - Metelytsia, Larisa
AU  - Metelytsia L
LA  - eng
PT  - Journal Article
PL  - United Arab Emirates
TA  - Curr Drug Discov Technol
JT  - Current drug discovery technologies
JID - 101157212
RN  - 0 (Antifungal Agents)
RN  - 0 (Imidazoles)
RN  - 0 (Ionic Liquids)
SB  - IM
MH  - Antifungal Agents/chemistry/*pharmacology
MH  - Candida albicans/*drug effects/growth & development
MH  - Imidazoles/chemistry/*pharmacology
MH  - Ionic Liquids/chemistry/*pharmacology
MH  - Machine Learning
MH  - *Models, Molecular
MH  - Neural Networks, Computer
MH  - Quantitative Structure-Activity Relationship
MH  - Regression Analysis
MH  - Reproducibility of Results
EDAT- 2016/05/11 06:00
MHDA- 2017/10/04 06:00
CRDT- 2016/05/11 06:00
PHST- 2016/02/29 00:00 [received]
PHST- 2016/04/15 00:00 [revised]
PHST- 2016/05/06 00:00 [accepted]
PHST- 2016/05/11 06:00 [entrez]
PHST- 2016/05/11 06:00 [pubmed]
PHST- 2017/10/04 06:00 [medline]
AID - CDDT-EPUB-75555 [pii]
AID - 10.2174/1570163813666160510122201 [doi]
PST - ppublish
SO  - Curr Drug Discov Technol. 2016;13(2):109-19. doi: 
      10.2174/1570163813666160510122201.