Quantitative structure-activity relationship (QSAR)
GPTKB entity
Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
computational chemistry method
predictive modeling technique |
| gptkbp:appliesTo |
polymers
small molecules peptides |
| gptkbp:basedOn |
statistical modeling
molecular descriptors |
| gptkbp:field |
pharmaceutical sciences
toxicology chemoinformatics |
| gptkbp:firstDescribed |
gptkb:Corwin_Hansch
1960s |
| gptkbp:goal |
reduce animal testing
accelerate drug development predict pharmacodynamics predict pharmacokinetics predict toxicity |
| gptkbp:hasJournal |
Journal of Computer-Aided Molecular Design
QSAR & Combinatorial Science |
| https://www.w3.org/2000/01/rdf-schema#label |
Quantitative structure-activity relationship (QSAR)
|
| gptkbp:limitation |
overfitting
data quality interpretability applicability domain |
| gptkbp:output |
gptkb:logic
|
| gptkbp:regulates |
gptkb:OECD_guidelines
REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) |
| gptkbp:relatedTo |
quantitative structure-property relationship (QSPR)
structure-activity relationship (SAR) |
| gptkbp:requires |
chemical structure data
biological activity data |
| gptkbp:software |
gptkb:KNIME
MOE (Molecular Operating Environment) QSAR Toolbox |
| gptkbp:usedFor |
drug discovery
environmental risk assessment predicting biological activity of molecules |
| gptkbp:uses |
gptkb:machine_learning
gptkb:principal_component_analysis gptkb:partial_least_squares_regression neural networks decision trees linear regression support vector machines random forest molecular fingerprints physicochemical properties topological indices |
| gptkbp:bfsParent |
gptkb:Corwin_Hansch
|
| gptkbp:bfsLayer |
8
|