In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program’s NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.
To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemicalpharmacological space diversity. Such libraries include billions of structural formulae of druglike molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are "Big Data". Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits' information has been accepted by the European Initiative "JEDI Grand Challenge against COVID-19" for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed.
An outbreak of a new coronavirus disease (COVID-19) in China in December 2019 became the epicenter for the spread of a global pandemic. The SARS-CoV-2 coronavirus causes a cascade of respiratory diseases similar to severe acute respiratory syndrome (SARS). Currently, there is no effective, specific, and safe treatment for COVID-19 to suppress the virus in the human body. The present study searched for pharmacological substances with antiviral activity for possible drug repositioning based on experimental and theoretical information in a series of publications on in vitro assays of agents against SARS-CoV-2. An analysis identified 46 well-known pharmaceutical substances that could be used for drug repositioning to create a therapy for COVID-19.
Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions. We have evaluated the reasonability of this approach by applying molecular descriptors (MNA) for structural presentation of the peptide segments. Comparative testing was performed using the prognostic method PASS and two input data types: sets of the MNA descriptors represented peptides as chemical structures and amino acid sequences written using a one-letter code. Training sets were classified in accordance with the established types of the enzymes (protein kinases), modifying corresponding phosphorylation sites. The accuracy estimates obtained by prognosis validation for various classes of substrates were significantly different with both the letters and molecular descriptors. In case of the letter description, the prognosis accuracy demonstrated less dependence on the length of peptides in the training set, while in the case of structural descriptors the accuracy level was determined by the peptide size and descriptor characteristics (MNA levels). The maximal prognosis accuracy related to various kinase families was achieved at different sizes of molecular fragments covered by the MNA descriptors of corresponding levels. This obviously reflected structural differences in surroundings of phosphorylation sites modified by various protein kinases. The use of molecular descriptors provided the prognostic results comparable with the results obtained using traditional letter representation. The prognosis accuracy demonstrated less dependence on the method describing site-surrounding peptides at higher accuracy rates. Applying the MNA descriptors it is possible to achieve better accuracy in the cases when the letter description cannot provide acceptable accuracy.
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