RDN-package: RDN: Reliability Density Neighborhood for Applicability...

In machLearnNA/RDN: Package for the Calculation of the Reliability-Density Neighbourhood (RDN) Applicability Domain Technique for QSAR Classification Models

Description

The RDN package provides a straightforward way of computing a QSAR model's applicability domain (AD), being currently only applicable for classification models. This method scans the chemical space, starting from the locations of training instances, taking into account local density, and local bias and precision. After the chemical space has been mapped, the established RDN AD can be used to sort new (external) predictions according to their reliability. Even though the RDN mapping is calculated using getRDN, the different tasks that this entails are separately available through the remaining functions in the package, which are listed below. However, Despite being available for use functions should ideally not be called isolated, and the user should use getRDN directly instead.

Details

The AD will be established according to the following workflow:

• STEP #1: Calculation of an Euclidean Distance matrix of the training set through getEDmatrix. This matrix will contain the distance between each training instance and each of its training neighbours, sorted in ascending order of distance.

• STEP #2: Calculation of individual average distance to the k-th nearest neighbours through getThreshold. This distance will be used as coverage threshold around each training instance.

• STEP #3: Place new queries onto the established coverage map using TestInTrain. If an instance is located within the radius of coverage around any training instances, it will be deemed as covered by the AD.

This workflow is fully automated in getRDN which runs these steps iteratively for a range of k values, which allows scanning chemical space from the near vicinity around training instances outwards. The full details on the theoretical background of this algorithm are available in the literature.[1]

References

[1] N Aniceto, AA Freitas, et al. A Novel Applicability Domain Technique for Mapping Predictive Reliability Accross the Chemical Space of a QSAR: Reliability-Density Neighbourhood. J Cheminf. 2016. Submitted.

machLearnNA/RDN documentation built on May 21, 2019, 10:51 a.m.