Design of experiments applied to QSAR: ranking a set of compounds and establishing a statistical significance test

Barroso, Juan Manuel; Besalú, Emili

doi:10.1016/j.theochem.2005.02.051

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Many times the original property is not binary, and in these cases the SSIR method requires an arbitrary threshold frontier. Once the available molecular set presents only two classes, associating a significance p -value to each rule is immediate [ 21 , 22 , 23 ]. The calculation involves the number of molecules of interest which are present (known) in the library and are condensed by the rule: if the known sublibrary consists of a ≤ M analogues, being b of them declared of interest, a particular rule condensing c known molecules has the following hypergeometric probability to condense d out of c being also of interest:

…”

Section: Methodsmentioning

confidence: 99%

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

Besalú

2016

IJMS

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The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.

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…”

Section: Methodsmentioning

confidence: 99%

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

Besalú

2016

IJMS

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“…[29,30] The activities where codified in percentage (values ranging from 0 up to 100) in such a way that the higher the value, the higher the activity. This complete set was previously studied by means of Formal Inference-based Recursive Modeling (FIRM) by Young and Hawkins [31] and with classical Design of Experiments by Barroso and Besalú [18] arriving to similar conclusions. The peptides structure is -2] appear to be a secondary interaction.…”

Section: Results and Discussion: Application Examplementioning

confidence: 77%

“…The significance of a rule is defined from the hypergeometric formula. [17][18][19] Let us suppose that the library is composed by a  M known molecules, and b of them are of interest. Then, if a rule condenses c of those known compounds, d of them being also of interest, one can ask for the probability of this event (see Figure 2a for a simple representation of the subsets involved).…”

Section: Probabilistic Significance Attached To a Variablementioning

confidence: 99%

Superposing Significant Interaction Rules (SSIR) Method: A simple Procedure for Rapid Ranking of Congeneric Compounds

Besalú¹,

Pogliani²,

Julián-Ortiz³

2016

Croat. Chem. Acta

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The Superposing Significant Interaction Rules (SSIR) method is revised and implemented. The method is a simple combinatorial procedure, which deals with in situ generated rules among a dichotomized congeneric molecular family, selecting the most probabilistically relevant ones. The mere counting of the number of relevant rules attached to new compounds generates a molecular ranking useful for database filtering, refinement and prediction. The algorithm only needs for a symbolic molecular representation and this allows for mining the database in a confidential manner. Third parties will not know the real compounds that are on the way to be worked out. The procedure is tested for a complete series of substituted amino acids. Areas under the receiver operating characteristic (AU-ROC) are always greater than 0.9 for all the following tried protocols: training, leave-one out, balanced leave-two-out and 5-fold cross validations and, finally, a stochastic series of calculations combined with a randomization test.

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2009

Molecular Descriptors for Chemoinformatics

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Design of experiments applied to QSAR: ranking a set of compounds and establishing a statistical significance test

Cited by 9 publications

References 12 publications

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

Superposing Significant Interaction Rules (SSIR) Method: A simple Procedure for Rapid Ranking of Congeneric Compounds

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