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.

[1]  Matteo Biolatti,et al.  Human Cytomegalovirus and Autoimmune Diseases: Where Are We? , 2021, Viruses.

[2]  Egon L. Willighagen,et al.  The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching , 2017, Journal of Cheminformatics.

[3]  Igor V. Tetko,et al.  Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program , 2002, J. Chem. Inf. Comput. Sci..

[4]  Igor V. Tetko,et al.  Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..

[5]  V. Zhirnov,et al.  Synthesis and in vitro anticytomegalovirus activity of 5-hydroxyalkylamino-1,3-oxazoles derivatives , 2020, Medicinal Chemistry Research.

[6]  P. Griffiths,et al.  The pathogenesis of human cytomegalovirus , 2015, The Journal of pathology.

[7]  M. Grimmett 4.07 – Imidazoles and their Benzo Derivatives: (ii) Reactivity , 1984 .

[8]  Igor V. Tetko,et al.  ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions , 2012, J. Chem. Inf. Model..

[9]  I. Tetko,et al.  Applicability domain for in silico models to achieve accuracy of experimental measurements , 2010 .

[10]  M. Prichard,et al.  A standardized approach to the evaluation of antivirals against DNA viruses: Orthopox‐, adeno‐, and herpesviruses , 2018, Antiviral research.

[11]  Igor V. Tetko,et al.  Transformer-CNN: Swiss knife for QSAR modeling and interpretation , 2020, Journal of Cheminformatics.

[12]  John T Brooks,et al.  Guidelines for prevention and treatment of opportunistic infections in HIV-infected adults and adolescents: recommendations from CDC, the National Institutes of Health, and the HIV Medicine Association of the Infectious Diseases Society of America. , 2009, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[13]  Igor V. Tetko,et al.  Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process , 2014, Journal of Cheminformatics.

[14]  In vitro activity of novel derivatives of 1,3-oxazole-4-carboxylate and 1,3-oxazole-4-carbonitrile against human cytomegalovirus , 2019, Medicinal Chemistry Research.

[15]  Lemont B. Kier,et al.  Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information , 1995, J. Chem. Inf. Comput. Sci..

[16]  Noel Southall,et al.  Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models , 2019, J. Chem. Inf. Model..

[17]  I. Tetko,et al.  Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing , 2018, Chemical biology & drug design.

[18]  Martyn G. Ford,et al.  Unsupervised Forward Selection: A Method for Eliminating Redundant Variables , 2000, J. Chem. Inf. Comput. Sci..

[19]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[20]  J. Engberts,et al.  The mannich condensation of sulfinic acid with aldehydes and carboxamides, sulfonamides, or lactams. Part IV , 2010 .

[21]  Igor V. Tetko,et al.  Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection , 2008, J. Chem. Inf. Model..