In multidimensional property space, compound clusters make classes separated by distance {cluster analysis} (CA). CA reduces unimportant variables. Substructure, topological index, physico-chemical property, calculated physico-chemical property, or hydrophobicity can determine classes.
Using discrete or continuous data and embedded data can put compounds into groups by activity level {cluster significance analysis} (CSA). CSA locates small clusters in large spaces.
Methods {Cone and Hodgkin similarity index} can measure molecular similarity.
Models {discriminant-regression model} (DIREM) can locate small clusters in large spaces.
Methods {distance-b program} (EVE) can locate small clusters in large spaces.
Unsupervised methods {hierarchical cluster analysis} (HCA) can measure distances between all points and make point vs. distance dendograms.
Structures can cluster in large databases by rating different compounds by similarity {Jarvis-Patrick method}.
Supervised methods {k-nearest neighbor} (k-NN) can calculate new-object distances from all other objects, to locate small clusters in large spaces.
Processes {partitioning} can merge individuals into groups or split whole into clusters.
Values {similarity measure} can compare distances.
Methods {single class discrimination} (SCD) can locate small clusters in large spaces.
Classifications {supervised method} can use already known patterns and clusters.
Activity and descriptor correlation vectors {trend vector analysis} can rank compound similarity.
Hierarchical methods {Ward's clustering method} {Ward clustering method} can agglomerate compounds to find clustering.
Supervised methods {Soft Independent Modeling of Class Analogies} (SIMCA) can use region-boundary or envelope models, to locate small clusters in large spaces.
Clustering methods {class analogy} can be SIMCA methods.
Distance measures {city-block distance} between structure-space points can be the same as Manhattan distance.
Distance measures {Manhattan distance} between structure-space points can be the same as city-block distance.
Distance measures {Minkowski distance} between structure-space points can be the same as Lp-metric.
Distance measures {Lp-metric} between structure-space points can be the same as Minkowski distance.
Structure-space points have distances {Mahalanobis distance}.
Hierarchical methods {centroid linkage} that agglomerate compounds can find clustering.
Hierarchical methods {complete linkage} that agglomerate compounds can find clustering.
Hierarchical methods {single linkage} that agglomerate compounds can find clustering.
5-Chemistry-Biochemistry-Drug-Activity-Methods
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Date Modified: 2022.0225