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Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies (BA) of 59-98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60-99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti-E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti-E. coli activity., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs., Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation., Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains., Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61-78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67-79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10-100 mg/L) and one as practically harmless (LD50 in the range of 100-1000 mg/L)., Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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 2  = .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., (© 2018 John Wiley & Sons A/S.)

Based on modern literature data about biological activity of E7010 derivatives, a series of new sulfonamides as potential anticancer drugs were rationally designed by QSAR modeling methods Сlassification learning QSAR models to predict the tubulin polymerization inhibition activity of novel sulfonamides as potential anticancer agents were created using the Online Chemical Modeling Environment (OCHEM) and are freely available online on OCHEM server at https://ochem.eu/article/107790. A series of sulfonamides with predicted activity were synthesized and tested against 60 human cancer cell lines with growth inhibition percent values. The highest antiproliferative activity against leukemia (cell lines K-562 and MOLT-4), non-small cell lung cancer (cell line NCI-H522), colon cancer (cell lines NT29 and SW-620), melanoma (cell lines MALME-3M and UACC-257), ovarian cancer (cell lines IGROV1 and OVCAR-3), renal cancer (cell lines ACHN and UO-31), breast cancer (cell line T-47D) was found for compounds 4-9. According to the docking results the compounds 4-9 induce cytotoxicity by the disruption of the microtubule dynamics by inhibiting tubulin polymerization via effective binding into colchicine domain, similar the E7010., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The disturbing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has been driving the scientific community to urgently search for new and efficient antitubercular drugs. Despite the various drugs currently under evaluation, isoniazid is still the key and most effective component in all multi-therapeutic regimens recommended by the WHO. This paper describes the QSAR-oriented design, synthesis and in vitro antitubercular activity of several potent isoniazid derivatives (isonicotinoyl hydrazones and isonicotinoyl hydrazides) against H37Rv and two resistant Mtb strains. QSAR studies entailed RFs and ASNNs classification models, as well as MLR models. Strict validation procedures were used to guarantee the models' robustness and predictive ability. Lipophilicity was shown not to be relevant to explain the activity of these derivatives, whereas shorter N-N distances and lengthy substituents lead to more active compounds. Compounds 1, 2, 4, 5 and 6, showed measured activities against H37Rv higher than INH (i.e., MIC ≤ 0.28 μM), while compound 9 exhibited a six fold decrease in MIC against the katG (S315T) mutated strain, by comparison with INH (i.e., 6.9 vs. 43.8 μM). All compounds were ineffective against H37RvINH (ΔkatG), a strain with a full deletion of the katG gene, thus corroborating the importance of KatG in the activation of INH-based compounds. The most potent compounds were also shown not to be cytotoxic up to a concentration 500 times higher than MIC., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)

Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74-0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71-0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.

A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Antituberculosis activity of several 5-(4-aminophenyl)-4-alkyl/aryl-2,4-dihydro-3H-1,2,4-triazole-3-thiones (1-9) and their thiourea derivatives (10-31) were screened for their antimycobacterial activities against Mycobacterium tuberculosis H37Rv using the BACTEC 460 radiometric system. Of the synthesized compounds, 10-12, 30 were the most active derivatives exhibiting more than 90 % inhibition of mycobacterial growth at 12.5 microg/mL. Structure-activity relationships study was performed for the given series by using the Electronic-Topological Method combined with Neural Networks (ETM-NN). A system of prognosis was developed as the result of training associative neural network (ASNN) using weights of pharmacophoric fragments as descriptors. Descriptors were calculated by the projection of ETM compound and pharmacophoric fragments on the elements of Kohonen's self-organizing maps (SOM). From the detailed analysis of all compounds under study, the necessary requirements for a compound to possess antituberculosis activity were formulated. The analysis have shown that any requirements violation for a molecule implies a considerable decrease or even complete loss of its activity.

Microtubules play a significant role in cell growth and functioning. Therefore inhibition of the microtubule assemblies has emerged as one of the most promising cancer treatment strategies. Predictive QSAR models were built on a series of selective inhibitors of the tubulin were performed by using Associative Neural Networks (ANN). To overcome the problem of data overfitting due to the descriptor selection, a 5-fold cross-validation with variable selection in each step of the analysis was used. All developed QSAR models showed excellent statistics on the training (total accuracy: 0.96–0.97) and test sets (total accuracy: 0.95–97). The models were further validated by 11 synthesized 1,3-oxazole derivatives and all of them showed inhibitory effect on the Hep-2 cancer cell line. The most promising compound showed inhibitory activity IC 50 = 60.2 μM. In order to hypothesize their mechanism of action the top three compounds were docked in the colchicine binding site of tubulin and showed reasonable docking scores as well as favorable interactions with the protein. [ABSTRACT FROM AUTHOR]