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A total of 4638 molecules from a pool of 238,819 molecules were identified as hits while 297 molecules were indicated as highly active.
This model was validated against 110 known ZAP-70 inhibitors with a correlation of 0.902 as well as enrichment factor of 1.61 against a maximum value of 2. This model picked 4094 hits from a database of 238,819 molecules while 358 molecules were indicated as highly active.
Possible hydrophobic and electrostatic interaction points in dynamic complexes of these molecules were indicated by estimated binding affinity energies of −6.6±0.4 kJ/mol for OT-GKR and −11.8±0.6 kJ/mol for OT.
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Sample and standard molecules are indicated.
Selected viable molecules are indicated by horizontal lines.
The number of water molecules is indicated by the number of H2O.
The binding affinity of dye molecules is indicated by high k L values.
In this image, scaffolds of bioactive molecules are indicated by magenta background, where the color intensity is proportional to the ratio between bioactive and all molecules containing this scaffold.
All molecules are indicated as important in tumorigenesis as well as other pathological states e.g. diabetes.
Most of these molecules are indicated as important in tumor genesis as well as other pathological states.
Correlated relationships (PCC > 0.9) are represented by solid lines, and the text-mining results between pairs of input molecules and literature-derived molecules are indicated by dotted lines.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com