Abstract
Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
Highlights
Adverse drug reactions (ADRs) are one of the main problems in drug discovery and clinical practice (Böhm and Cascorbi, 2016)
(Q)SAR Modeling of Antitarget Inhibitors were created by GUSAR software for each from five training sets with internal validation
If R2 of internal validation for (Q)SAR model was less than 0.5, the model was excluded from the final consensus model [excluding QSAR models for D(1A) and D(2) dopamine receptors, histamine H1 and 5-hydroxytryptamine 2B receptors created on the basis of IC50 data]
Summary
Adverse drug reactions (ADRs) are one of the main problems in drug discovery and clinical practice (Böhm and Cascorbi, 2016). In silico approaches are usually based on machine learning techniques and network analyses to link several chemical and biological features of approved and withdrawn drugs to ADRs, which include molecular descriptors, known or predicted drug targets, drug-induced gene expression profiles and cell phenotypic features (Ivanov et al, 2016). These approaches allow predict dangerous ADRs in the early stages of drug development and provide insights into potential toxic mechanisms of drug candidates. We have significantly expanded the list of covered “antitargets” and significantly increased the volumes and diversity of training samples, which allowed us to expand
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