Sentence examples for compound query from inspiring English sources
Some of them asked tough, pointed questions; others seemed unable to compose anything but a tortured compound query.
This model can then be applied to reason about the applicability of a model when applied to a new compound query.
In the case of the PubChem Compound database, there is a filter "has 3d conformer" that will indicate whether a given compound record has a 3-D conformer model by means of the PubChem Compound query: ""has 3d conformer"[filter]".
When human experts try to predict the activity of an unseen compound (query compound) they usually analyse the chemical structure and search for parts of their knowledge that can be applied to the structure.
Table 1 Different 3-D similarity search (or analysis) scenarios considered Search Scenario Query Conformer model Description A Compound Single conformer Similarity scores for a compound "query" compared to those of the "database" compounds, both computed using a single conformer per compound.
For example, in the case shown in Fig. 1, two component queries, "JAMES C BEST JR THE NEW YORK TIMES" from the first body block and "It was the best of Twitter It" from the second, are combined into one compound query, i.e., "JAMES C BEST JR THE NEW YORK TIMES" "It was the best of Twitter It".
Thus, we propose composing Compound queries rather than Simple queries for body blocks.
As shown in Fig. 2c, for body blocks, we use compound queries, whereas, for title and other blocks, we still use simple queries.
Input compound queries are converted into 2048 bit Morgan fingerprints using Rdkit [52], imported into NumPy vectors, and passed to the BernoulliNB Scikit-learn module.
We then formulate compound queries by (1) selecting two body blocks containing any unused component query and (2) combining a component query in the first block with a space character and another component query from the second one.
To formulate compound queries, we first formulate half-length simple queries by Algorithm 3, whose length is between (sigma _{mathrm {qmax}}/2) and (sigma _{mathrm {qmin}}/2), from each body block Bi as their component query set (C_i).
