Symbol hatN_hits
hatits is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Read this term in its guide →Published equation contexts
Why this formula appears here
High-throughput virtual screening takes the opposite approach: rather than proposing new compositions, it exhaustively scores a library that already exists. The clearest demonstration is ultra-large library docking, in which researchers computationally docked a library of 170 million make-on-demand compounds, built from 130 well-characterized reactions, against two protein targets: AmpC beta-lactamase and the D4 dopamine receptor [ 8 ] . Against AmpC, 99 million library members were scored, 51 top-ranked compounds were selected and 44 synthesized, yielding an 11% hit rate and, after optimization, a 77-nanomolar inhibitor among the most potent non-covalent AmpC inhibitors then known. Against…
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Symbol s
s appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.
Read this term in its guide →Symbol h
the hit rate measured directly among compounds actually synthesized and tested at docking-score bin s.
Read this term in its guide →Starting index or lower bound: s
This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.
Read this term in its guide →How to interpret it
Its accuracy depends on the assumptions and range of use described in the article.
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Published contexts (1)
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 1 · AI for Science
Comparing the Main Approaches to AI for Science and Medicine
This equation gives an approximation: it relates the quantities while allowing an approximation.
High-throughput virtual screening takes the opposite approach: rather than proposing new compositions, it exhaustively scores a library that already exists. The clearest demonstration is ultra-large library docking, in which researchers computationally docked a library of 170 million make-on-demand compounds, built from 130 well-characterized reactions, against two protein targets: AmpC beta-lactamase and the D4 dopamine receptor [ 8 ] . Against AmpC, 99 million library members were scored, 51 top-ranked compounds were selected and 44 synthesized, yielding an 11% hit rate and, after optimization, a 77-nanomolar inhibitor among the most potent non-covalent AmpC inhibitors then known. Against…
Meanings in this article
- : the hit rate measured directly among compounds actually synthesized and tested at docking-score bin s.
- : the number of untested library members scored into that bin.