predictChemPC

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Description

This function performs a prediction method from 4 predicting potent compounds methods of this package. These methods are RA, EI, NN and GP.

Usage

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predictChemPC(xTrain, yTrain, xTest, loghyper, method="RA")

Arguments

xTrain

m * n martrix of train data.

yTrain

m * 1 matrix of target values consist of potencies, pIC50 or other measurements of compound affinities that are desired to be maximized.

xTest

j * n matrix of test data.

loghyper

3 * 1 matrix of loghyper parameters which is the output of trainChemPC function.

method

One of "EI", "GP", "NN" or "RA".

Details

This function withholds 4 methods to predict potent compounds.

method is one of: EI A compound for which maximum expected potency improvement is reached. GP A compound holding maximum predicted potency in test data is selected. NN A compound that is nearest (Tonimito Coefficient as distance measure) to the most potent compound in training data is selected. RA As it's name suggests, a compound is selected randomly.

Feature selection Feature selection employed in this package is based on Spearman Rank Correlation such that before each training step those attributes in which revealed a significant Spearman rank correlation with the logarithmic potency values (q-value < 5 are computed from original p-values via the multiple testing correction method by Benjamini and Hochberg.

Value

It returns index of most potent compound in original test set w.r.t. selected method.

Author(s)

Mohsen Ahmadi

References

1.Predicting Potent Compounds via Model-Based Global Optimization, Journal of Chemical Information and Modeling, 2013, 53 (3), pp 553-559, M Ahmadi, M Vogt, P Iyer, J Bajorath, H Froehlich. 2.Software MOE is used to calculate the numerical descriptors in data sets. Ref: http://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm 3.ChEMBL was the source of the compound data and potency annotations in data sets. Ref: https://www.ebi.ac.uk/chembl/

Examples

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x = as.data.frame(array(1:100, dim=c(20,5)))
y = as.matrix(as.numeric(array(1:20, dim=c(20,1))))
xstar = as.data.frame(array(5:105, dim=c(20,5)))
loghyper = trainChemPC(x, y)
index = predictChemPC(x, y, xstar, loghyper, method="RA")

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