In Silico Design and in Vitro Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents.

QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2 = 0.79 ± 0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2 = 0.82 ± 0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.

Anticancer activity features of imidazole-based ionic liquids and lysosomotropic detergents: in silico and in vitro studies.

Long-chain imidazole-based ionic liquids (compounds 2, 4, 9) and lysosomotropic detergents (compounds 7, 3, 8) with potent anticancer activity were synthesized. Their inhibitory activities against neuroblastoma and leukaemia cell lines were predicted by the new in silico QSAR models. The cytotoxic activities of the synthesized imidazole derivatives were investigated on the SK-N-DZ (human neuroblastoma) and K-562 (human chronic myeloid leukaemia) cell lines. Compounds 2 and 7 showed the highest in vitro cytotoxic effect on both cancer cell lines. The docking procedure of compounds 2 and 7 into the NAD+ coenzyme binding site of deacetylase Sirtuin-1 (SIRT-1) showed the formation of protein-ligand complexes with calculated binding energies of - 8.0 and - 8.1 kcal/mol, respectively. The interaction of SIRT1 with compounds 2, 7 and 9 and the interaction of Bromodomain-containing protein 4 (BRD4) with compounds 7 and 9 were also demonstrated by thermal shift assay. Compounds 2, 4, 7 and 9 inhibited SIRT1 deacetylase activity in the SIRT-Glo assay. Compounds 7 and 9 showed a moderate inhibitory activity against Aurora kinase A. In addition, compounds 3, 4, 8 and 9 inhibited the Janus kinase 2 activity. The results obtained showed that long-chain imidazole derivatives exhibited cytotoxic activities on K562 leukaemia and SK-N-DZ neuroblastoma cell lines. Furthermore, these compounds inhibited a panel of molecular targets involved in leukaemia and neuroblastoma tumorigenesis. All these results suggest that both long-chain imidazole-based ionic liquids and lysosomotropic detergents may be an effective alternative for the treatment of neuroblastoma and chronic myeloid leukemia and merit further investigation.

In Silico, in Vitro and in Vivo Study of Substituted Imidazolidinone Sulfonamides as Antibacterial Agents.

New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested in vitro. Six studied compounds have shown the highest in vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The in vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35 mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.

Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains.

To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from -8.4 to -9.4 kcal/mol.

Structure-Activity Relationship Prediction-Based Synthesis and Cytotoxicity Evaluation against the HEp-2 Laryngeal Carcinoma Cell of Isoflavone-Cytisine Mannich Bases.

QSAR analysis of previously synthesized and nature-inspired virtual isoflavone-cytisine hybrids against the HEp-2 laryngeal carcinoma cell lines was performed using the OCHEM web platform. 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 such as 8-cytisinylmethyl derivatives of 5,7- and 6,7-dihydroxyisoflavones. The synthetic procedure for selective aminomethylation of 5,7-dihydroxyisoflavones with cytisine was developed. In vitro testing identified compound 7 f with cisplatin-level cytotoxicity against HEp-2 cell lines and compound 10 which was twice active than cisplatin after 72 h of incubation.

In Silico Design and Experimental Validation of Novel Oxazole Derivatives Against Varicella zoster virus.

Varicella zoster virus (VZV) infection causes severe disease such as chickenpox, shingles, and postherpetic neuralgia, often leading to disability. Reactivation of latent VZV is associated with a decrease in specific cellular immunity in the elderly and in patients with immunodeficiency. However, due to the limited efficacy of existing therapy and the emergence of antiviral resistance, it has become necessary to develop new and effective antiviral drugs for the treatment of diseases caused by VZV, particularly in the setting of opportunistic infections. The goal of this work is to identify potent oxazole derivatives as anti-VZV agents by machine learning, followed by their synthesis and experimental validation. Predictive QSAR models were developed using the Online Chemical Modeling Environment (OCHEM). Data on compounds exhibiting antiviral activity were collected from the ChEMBL and uploaded in the OCHEM database. The predictive ability of the models was tested by cross-validation, giving coefficient of determination q2 = 0.87-0.9. The validation of the models using an external test set proves that the models can be used to predict the antiviral activity of newly designed and known compounds with reasonable accuracy within the applicability domain (q2 = 0.83-0.84). The models were applied to screen a virtual chemical library with expected activity of compounds against VZV. The 7 most promising oxazole derivatives were identified, synthesized, and tested. Two of them showed activity against the VZV Ellen strain upon primary in vitro antiviral screening. The synthesized compounds may represent an interesting starting point for further development of the oxazole derivatives against VZV. The developed models are available online at OCHEM http://ochem.eu/article/145978 and can be used to virtually screen for potential compounds with anti-VZV activity.

Theoretical and Experimental Studies of Phosphonium Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii.

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.

Synthesis, QSAR modeling, and molecular docking of novel fused 7-deazaxanthine derivatives as adenosine A2A receptor antagonists.

Predictive QSAR models for the search of new adenosine A2A receptor antagonists were developed by using OCHEM platform. The predictive ability of the regression models has coefficient of determination q2  = 0.65-0.71 with cross-validation and independent test set. The inhibition activities of novel fused 7-deazaxanthine compounds were predicted by the developed QSAR models. A preparative method for the synthesis of pyrimido[5',4':4,5]pyrrolo[1,2-a][1,4]diazepine derivatives was developed, and 11 new adenosine A2A receptor antagonists were obtained. Preliminary investigations into the toxicology of fused 7-deazaxanthine compounds toward commonly used model organism to assess toxicity invertebrate cladoceran D. magna were also described.

Design of new imidazole derivatives with anti-HCMV activity: QSAR modeling, synthesis and biological testing.

The problem of designing new antiviral drugs against Human Cytomegalovirus (HCMV) was addressed using the Online Chemical Modeling Environment (OCHEM). Data on compound antiviral activity to human organisms were collected from the literature and uploaded in the OCHEM database. The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2 = 0.71-0.76. 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 reasonable accuracy within the applicability domain (q2 = 0.70-0.74). The models were applied to screen a virtual chemical library of imidazole derivatives, which was designed to have antiviral activity. The six most promising compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. However, only two of them showed some activity against the HCMV AD169 strain.

Structure-Activity Relationship Modeling and Experimental Validation of the Imidazolium and Pyridinium Based Ionic Liquids as Potential Antibacterials of MDR Acinetobacter Baumannii and Staphylococcus Aureus.

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 - 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.

1,3-Oxazole derivatives of cytisine as potential inhibitors of glutathione reductase of Candida spp.: QSAR modeling, docking analysis and experimental study of new anti-Candida agents.

Natural products as well as their derivatives play a significant role in the discovery of new biologically active compounds in the different areas of our life especially in the field of medicine. The synthesis of compounds produced from natural products including cytisine is one approach for the wider use of natural substances in the development of new drugs. QSAR modeling was used to predict and select of biologically active cytisine-containing 1,3-oxazoles. The eleven most promising compounds were identified, synthesized and tested. The activity of the synthesized compounds was evaluated using the disc diffusion method against C. albicans M 885 (ATCC 10,231) strain and clinical fluconazole-resistant Candida krusei strain. Molecular docking of the most active compounds as potential inhibitors of the Candida spp. glutathione reductase was performed using the AutoDock Vina. The built classification models demonstrated good stability, robustness and predictive power. The eleven cytisine-containing 1,3-oxazoles were synthesized and their activity against Candida spp. was evaluated. Compounds 10, 11 as potential inhibitors of the Candida spp. glutathione reductase demonstrated the high activity against C. albicans M 885 (ATCC 10,231) strain and clinical fluconazole-resistant Candida krusei strain. The studied compounds 10, 11 present the interesting scaffold for further investigation as potential inhibitors of the Candida spp. glutathione reductase with the promising antifungal properties. The developed models are publicly available online at http://ochem.eu/article/120720 and could be used by scientists for design of new more effective drugs.

In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii.

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 http://ochem.eu/article/113921 and could be used by scientists for design of new more effective antibiotics.

Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing.

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.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity.

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.

QSAR Study of Some 1,3-Oxazolylphosphonium Derivatives as New Potent Anti-Candida Agents and Their Toxicity Evaluation.

BACKGROUND: The incidence of invasive fungal infections caused by Candida spp. has increased continuously in recent decades, especially in populations of immunocompromised patients or individuals hospitalized with serious underlying diseases. Therefore, the goal of our study was the search for new potent Candida albicans inhibitors via the development of QSAR models that could speed up this search process. A number of the most promising 1,3-oxazol-4-yltriphenylphosphonium derivatives with predicted activities were synthesized and experimentally tested. Furthermore, the toxicity of the studied compounds was determined in vitro using acetylcholinesterase enzyme as a biological marker. METHODS: The classification QSAR models were created using Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks methods and different combinations of descriptors on the Online Chemical Modeling Environment (OCHEM) platform. Аntifungal properties of the investigated compounds were performed using standard disk diffusion method. The enzyme inhibitory action of the compounds was determined by modified Ellman's method using acetylcholinesterase from the electric organ of Electrophorus electricus. RESULTS: Three classification QSAR models were developed by the WEKA-RF, k-NN and ASNN methods using the ALogPS, E-State indices and Dragon v.7 descriptors. The predictive ability of the models was tested through cross-validation, giving a balanced accuracy BA = 80-91%. All compounds demonstrated good antifungal properties against Candida spp. and slight inhibition of the acetylcholinesterase activity. CONCLUSION: The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models that can be applied as tools for finding new potential inhibitors against Candida spp. Furthermore, 1,3-oxazol-4- yl(triphenyl)phosphonium salts could be considered as promising candidates for the treatment of candidiasis and the disinfection of medical equipment.

Design, synthesis and evaluation of novel sulfonamides as potential anticancer agents.

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.

Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

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.

Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies.

This paper describes Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity of several potent imidazolium-based ionic liquids (ILs) against S. aureus ATCC 25923 and its clinical isolate. Small set of 131 ILs was collected from the literature and uploaded in the OCHEM database. QSAR methodologies used Associative Neural Networks and Random Forests (WEKA-RF) methods. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.82-0.87 for regression models and overall prediction accuracies of 80-82.1% for classification models. The proposed QSAR models are freely available online on OCHEM server at https://ochem.eu/article/107364 and can be used for estimation of antibacterial activity of new imidazolium-based ILs. A series of synthesized 1,3-dialkylimidazolium ILs with predicted activity were evaluated in vitro. The high activity of 7 ILs against S. aureus strain and its clinical isolate was measured and thereafter analyzed by the molecular docking to prokaryotic homologue of a eukaryotic tubulin FtsZ.

Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform.

Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.