Synthesis and biological evaluation of DAPY–DPEs hybrids as non-nucleoside inhibitors of HIV-1 reverse transcriptase

Wu, Huawei; Yao, Jin; He, Qiuqin; Chen, Wenxue; Chen, Fen‐Er; Pannecouque, Christophe; Clercq, Erik De; Daelemans, Dirk

doi:10.1016/j.bmc.2014.11.032

Cited by 13 publications

(1 citation statement)

References 34 publications

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“…Unlike the CC 50 values, the IC 50 values of the studied compounds did not differ significantly. Also, in the publications by Boyapalle and coauthors, HIV-SF33 was tested in several bioassays with different cell types. , The EC 50 results were not significantly different, but the CC 50 values were about 1.5–2 times higher than for PBMCs cells. These findings may be tentatively explained by the fact that cytotoxicity itself is a complex biological phenomenon of interaction of a compound with many different molecular pathways, which can be significantly different in different cell lines.…”

Section: Resultsmentioning

confidence: 99%

Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications

Tarasova

Biziukova

Filimonov

et al. 2019

J. Chem. Inf. Model.

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A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure–activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.

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Section: Resultsmentioning

confidence: 99%