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Last, we note that enrichment of known ligands from virtual screening can be improved by rescoring the docked ligands with additional scoring functions.
This work shows that and how such structural similarity methods used in virtual screening can be improved further by integrating background knowledge on binding-relevant structural features.
In this paper, we present a concept of how structural similarity based methods used in virtual screening can be improved by integrating chemical background knowledge in the form of binding relevant or informative structural elements.
In addition, the performance of screening can be improved by adding more genotype data of affected individuals.
Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility.
The efficiency of PCR-based screening can be improved by pooling clone libraries in specific ways.
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The results suggest that children's learning from a screen can be improved by contingent, interactive experiences with media.
Because of the reduced size of such "protein family" collections relative to complete genomic collections of tens of thousands of entities, the quality of screens can be improved by testing samples in replicates, for example.
The significant overlap between our computational predictions and RNAi screening results suggests that RNAi screen results can be improved by using computational predictions to guide experiments, performing RNAi screens in size-reduced, prioritized subspaces predicted by our model, thus allowing more tissue types or experimental conditions to be tested with the same resources.
The above results also suggest that both dynamic foam analysis and rheometry as screening tools can be improved by including tests with oil along the experimental protocols.
We therefore, suggest that with the optimum coverage of biologically relevant scaffold space, hit rates in high throughput screening experiments can be improved.
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