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Pekka Tiikkainen Antti Poso Olli Kallioniemi 《Journal of computer-aided molecular design》2009,23(4):227-239
In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer
cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The
chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations
of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations
thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion)
led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their
ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY,
Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity
scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage
compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds
produced more consistent results than using the methods in isolation. 相似文献
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Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data. 相似文献
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Brookman Brian Butler Owen Ciaralli Laura Horsky Monika Laurent Caroline Noblett Tracey Örnemark Ulf Patriarca Marina Sibbesen Lorens P. Tiikkainen Ulla van Putten Kees Robouch Piotr 《Accreditation and quality assurance》2019,24(1):93-101
Accreditation and Quality Assurance - A summary of the working group discussions on proficiency testing (PT) and external quality assessment (EQA) held at the Eurachem Workshop, Portorož,... 相似文献
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