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Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials.

A previously developed model to predict antibacterial activity of ionic liquids against a resistant A. baumannii strain was used to assess activity of phosphonium ionic liquids. Their antioxidant potential was additionally evaluated with newly developed models, which were based on public data. The accuracy of the models was rigorously evaluated using cross-validation as well as test set prediction. Six alkyl triphenylphosphonium and alkyl tributylphosphonium bromides with the C8, C10, and C12 alkyl chain length were synthesized and tested in vitro. Experimental studies confirmed their activity against A. baumannii as well as showed pronounced antioxidant properties. These results suggest that phosphonium ionic liquids could be promising lead structures against A. baumannii.

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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 q = .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.

A series of new 1,3‐oxazole derivatives, containing in position 5 both donor and acceptor substituents were synthesized. These substances were considered as potentially active anticancer pharmacophores in the human tumor cell line panel derived from nine cancer types, including lung, colon, melanoma, renal, ovarian, brain, leukemia, breast, and prostate. Primary in vitro one‐dose anticancer screening was shown that compounds with acceptor substituents (such as –C(O)OMe, –CN) in the position 5 inhibit the growth of most cell lines, and compounds with donor substituents (such as –NHR, −SR) in the position 5 do not practically inhibit the growth of cancer cell lines. It can be assumed that the pharmacological activity of 1,3‐oxazole derivatives depends on donor/acceptor nature of the substituents in position 5. It was proposed to evaluate the donor/acceptor ability of 1,3‐oxazole derivatives using the special parameter φ0, which takes into account the relative position of the boundary levels (HOMO end LUMO). The quantum‐chemical modeling was performed; the special parameter φ0 for 1,3‐oxazole derivatives correlates with the experimental results. Quantum‐chemical calculations of the special parameter φ0 allow modeling the pharmacological activity of 1,3‐oxazole derivatives by introducing donor or acceptor substituents at position 2 or 5. This work may be useful for chemists to develop a target synthesis of potential biologically active compounds.