(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds [PDF]
Molecules, 2019 Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the ...
Leonid A. Stolbov +4 more
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Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors [PDF]
Frontiers in Pharmacology, 2018 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 ...
Alexey A. Lagunin +10 more
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Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. [PDF]
Molecules, 2019 Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious.
Buglak AA, Zherdev AV, Dzantiev BB.
europepmc +4 more sources
Meeting report, ICH M7 relevant workshop: use of (Q)SAR systems and expert judgment [PDF]
Genes and Environment, 2018 Use of Quantitative Structure-Activity Relationships ((Q)SAR) prediction tools has been increasing since the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline was issued in June 2014 ...
Masayuki Mishima +6 more
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The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7 [PDF]
Genes and Environment, 2020 The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary ...
Robert S. Foster +10 more
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In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR [PDF]
Frontiers in Pharmacology, 2019 A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking.
Gopal Pawar +4 more
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Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions [PDF]
Frontiers in Pharmacology, 2018 This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments.
Christoph Helma +7 more
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Analysis of Potential Q-Markers for Salt-Processed Alismatis Rhizoma in Diuresis Based on Fingerprinting Technology and Network Analysis [PDF]
Current Issues in Molecular BiologyIntroduction: The ability of salt-processed Alismatis Rhizoma (SAR) (Alisma plantago-aquqtica L.) to nourish Yin and promote urination is stronger than that of Alismatis Rhizoma (AR).
Lin Yan +5 more
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CheS-Mapper 2.0 for visual validation of (Q)SAR models [PDF]
J Cheminform, 2014 AbstractBackgroundSound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data.
Gütlein M, Karwath A, Kramer S.
europepmc +3 more sources
Bridging science and curriculum: preparing future leaders in computational toxicology [PDF]
Frontiers in ToxicologyComputational toxicology plays an important role in chemical safety assessments. Computational methods are applied to early-stage screening in drug discovery, hazard identification, and regulatory safety assessment.
Frances Hall, Candice Johnson
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