Linear regression analysis of inhibitory potency of organic disulfides against Histoplasma capsulatum

Lj, Schaad; Rh, Werner; Dillon, Lynn; L, Field; Ce, Tate

doi:10.1021/jm00238a004

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Linear regression is the foundation of many QSAR studies and serves as the basis for various state-of-the-art regression techniques, which are discussed in the following sections [1,[59][60][61][62][63]. For a one-dimensional input variable, the model is a line.…”

Section: Ordinary Least Squaresmentioning

confidence: 99%

Chemoinformatic regression methods and their applicability domain

Dutschmann,

Schlenker,

Baumann

2024

Molecular Informatics

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The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built‐in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

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Section: Ordinary Least Squaresmentioning

confidence: 99%

Chemoinformatic regression methods and their applicability domain

Dutschmann,

Schlenker,

Baumann

2024

Molecular Informatics

View full text Add to dashboard Cite

show abstract

“…for the unknowns µ, xx,..., yM by Cramer's rule. through N + 1 to row 2, which by property (d) of ref2…”

mentioning

confidence: 99%

“…However, it is clear that there will be trouble if the denominators vanish, that is, if detla,•; I = 0 (3) and this case must be examined more closely. If any row of det|a¡;| is a linear combination of the others, then by property (e) of ref 2 det|a,;| = 0. The converse is also true, so that eq 3 implies that some row (suppose it is the last) is a linear combination of the others.…”

mentioning

confidence: 99%