Processes have factors {factor analysis}. Physico-chemical or structural properties describe compounds and have components {descriptor, factor} {X-variable, factor} {X descriptor, factor}. Chemical activities relate to variables {response variable}.
Methods {canonical factor analysis} can be for factor analysis.
Methods {centroid method} can be for factor analysis.
QSAR {combinatoric QSAR} can find similarities using different descriptor combinations.
Moments of inertia, and dipole and quadrupole moments, can be descriptors to calculate molecular moments {Comparative Molecular Moment Analysis} (CoMMA). CoMMA depends on shapes and charges.
Properties and structures have relations {Correlation Analysis}.
Factor-analysis methods {correspondence analysis} {correspondence factor analysis} (CFA) can use variable frequencies relative to activities, finds chi-square values, and finds principal components.
Principal components {disjoint principal component} (DPP) can be independent.
Thresholds {eigenvalue-one criterion} can be how many components have eigenvalues greater than one.
Unsupervised linear methods {eigenvector projection} can find factors.
Models {Evolutionary Programming} (EP) can add and subtract randomly selected variables, with crossing-over, and evaluate for "fitness" or best fit.
Methods {evolving factor analysis} (EVA) can analyze ordered data.
Methods {percentage of explained variance} {explained variance percentage} can indicate number of components required to reach 90% of total variance.
Parameters and descriptors can linearly relate to free energy {extrathermodynamic approach}.
Factor-analysis methods {free energy perturbation} (FEP) can use free-energy changes.
Binary descriptors can note molecule-substructure presence or absence {Free-Wilson approach}.
Linear property sets can have different values, change values by crossing-over between related such genes, and have random change {Genetic Function Algorithm} (GFA), to select best fit.
Values {Hammett sigma value} can relate to electronic and electrostatic properties.
Activity, partition coefficients for hydrophobicity, ionization degree, and molecular size relate {Hansch equation}.
Variables {latent variable} can be linear-descriptor combination.
Supervised methods {linear discriminant analysis} (LDA), in which boundary surface minimizes region variance and maximizes variance between regions, can put compounds into groups by activity level.
log K = k1 * sigma + k2 {linear free energy equation, drug} (LFE).
Supervised methods {linear learning machine} (LLM) can divide n-dimensional space into regions, using discriminant function.
Factor-analysis methods {maximum-likelihood method} can find factors.
Metric or non-metric methods {multidimensional scaling} (MDS) can analyze similarity or dissimilarity matrices to find dimension number and place objects in proper relative positions.
Non-parametric methods {multivariate adaptive regression spline} (MARS) can find factors.
Models {Mutation and Selection Uncover Models} (MUSEUM) can add and subtract randomly selected variables, with no crossing-over, and evaluate for "fitness" or best fit.
Unsupervised linear methods {non-linear iterative partial least-squares} (NIPALS) can represent data as product of score matrix, for original observations, and loading-matrix transform, for original factors.
Topological mappings {non-linear mapping} (NLM) can be factor-analysis methods in which linear-variable combinations make two or three new variables.
Information about compound physico-chemical properties can predict compound chemical or physiological behavior in vitro and in vivo {predictive computational model}.
Variables {principal component} (PC) can be linear-descriptor combinations. Unsupervised linear method {principal component analysis, factor} (PCA) represents data as product of score matrix, for original observations, and loading-matrix transform, for original factors. PCA is factor-analysis method in which linear variable combinations make two or three new variables. PCA reduces unimportant variables.
Singular-value decomposition (SVD) can find best singular values for predicting {principal component regression} (PCR). SVD projects regression to latent structures.
Modified PCA {principal factor analysis} can find principal factors.
Methods {Procrustes analysis} can identify descriptor sets for describing similarity.
Methods {QR algorithm} can diagonalize matrices.
Unsupervised linear methods {rank annihilation} can find factors.
Residual variance approaches constancy {Scree-test, drug}, and plotted slope levels off {Scree-plot}, depending on component number.
In unsupervised linear methods {singular value decomposition, drug} (SVD), correlation matrix is product of score, eigenvalue, and loading matrices, with diagonalization using QR algorithm.
Factor-analysis methods {spectral mapping analysis} (SMA) can first take data logarithm to eliminate outliers and then subtract means from rows and columns, to leave only variation, showing which variables are important and how much.
Spaces {structure space} can have two or three principal components.
Methods {target-transformation factor analysis} can rotate features to match known pattern, such as hypothesis or signature.
Factors and response variable have relations {Unsupervised Method}, without using factor information or predetermined models.
Designs {factorial design} can try to ensure design-space sampling, if position varies.
Designs {fractional factorial design} can try to ensure design-space sampling, if position varies.
Three-level designs {response surface method} (RSM) can have three factors that quantify relationships among responses and factors. RSM includes MLR, OLS, PCR, and PLS linear designs; non-linear regression analysis (NLR); and non-parametric methods, such as ACE, NPLS, and MARS.
5-Chemistry-Biochemistry-Drug-Activity-Methods
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Date Modified: 2022.0225