TY  - JOUR
AB  - QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested against Gram-negative multidrug-resistant clinical isolate Acinetobacter baumannii was done using the Online Chemical Modeling Environment (OCHEM). To overcome the problem of overfitting due to descriptor selection, fivefold cross-validation with variable selection in each step of the model development was applied. The predictive ability of the classification models was tested by cross-validation, giving balanced accuracies (BA) of 76%-82%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 83%-89%). The models were applied to screen a virtual chemical library with expected activity of compounds against MDR Acinetobacter baumannii. The eighteen most promising compounds were identified, synthesized, and tested. Biological testing of compounds was performed using the disk diffusion method in Mueller-Hinton agar. All tested molecules demonstrated high anti-A. baumannii activity and different toxicity levels. The developed classification SAR models are freely available online at and could be used by scientists for design of new more effective antibiotics.
AU  - Trush, M.M.*
AU  - Kovalishyn, V.*
AU  - Hodyna, D.*
AU  - Golovchenko, O.V.*
AU  - Chumachenko, S.*
AU  - Tetko, I.V.
AU  - Brovarets, V.S.*
AU  - Metelytsia, L.*
C1  - 58575
C2  - 48365
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
SP  - 624-630
TI  - In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii.
JO  - Chem. Biol. Drug Des.
VL  - 95
IS  - 6
PB  - Wiley
PY  - 2020
SN  - 1747-0277
ER  - 

TY  - JOUR
AB  - The problem of designing new antitubercular drugs against multiple drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR‐TB, we collected a large literature data set and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 = .7–.8 (regression models) and balanced accuracies of about 80% (classification models) with cross‐validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR‐TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR‐TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti‐TB activity of new chemicals.
AU  - Kovalishyn, V.V.*
AU  - Grouleff, J.*
AU  - Semenyuta, I.*
AU  - Sinenko, V.O.*
AU  - Slivchuk, S.R.*
AU  - Hodyna, D.*
AU  - Brovarets, V.*
AU  - Blagodatny, V.*
AU  - Poda, G.*
AU  - Tetko, I.V.
AU  - Metelytsia, L.*
C1  - 53887
C2  - 45095
CY  - Po Box 211, 1000 Ae Amsterdam, Netherlands
SP  - 1272-1278
TI  - Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.
JO  - Chem. Biol. Drug Des.
VL  - 92
IS  - 1
PB  - Elsevier Science Bv
PY  - 2018
SN  - 1747-0277
ER  -