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The CSFP is a good way to analyze the diversity for large compound libraries.
Manual assignment of compounds to compound classes might be possible for some few thousand compounds but is impossible for large compound databases or structure data files.
Because of the inherent complexity of chemical structure comparisons, the challenges of standardising representations, and the difficulties of quality control for large compound collections, none of these sources are likely to claim their numbers as a standard of truth.
This is an important observation suggesting that though not so effective in actives enrichment with single pharmacophore model, iDrug enables fast prefiltering for large compound collections before applying more accurate and computationally expensive algorithms.
However, to get meaningful quantitative information, readout from a large population of cells for both Δ ψm loss and caspase activation is essential for large compound library screening.
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It must be noted, though, that this running time improvement is only relevant for large compounds: The smallest compound considered in [109, 110, 132] has mass above 1000 Da, and significant running time differences for Fourier are observed only for compounds with mass above 10 kDa.
The group independence assumption can be overly simplistic, especially for large compounds or in conjugated systems.
Possibly, gel filtration might be suitable for larger compounds (e.g., antibodies, proteins), since the performance of size exclusion chromatography is expected to be superior for larger compounds.
We found a strong dependence of enrichment on compound mass, indicating a higher activity on average for larger compounds.
For larger compounds across all MW ranges, no significant increases in promiscuity were observed compared to the global degree and probability of compound promiscuity reported in Table 1 and Table 2, respectively.
SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.
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