The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Awale, Mahendra; Reymond, Jean-Louis (2019). The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning. Journal of chemical information and modeling, 59(1), pp. 10-17. American Chemical Society 10.1021/acs.jcim.8b00524

Preview	Text acs.jcim.8b00524.pdf - Accepted Version Available under License Publisher holds Copyright. Download (1MB) | Preview
	Text acs.jcim.8b00524.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (2MB)

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools.

Interest & Impact

Downloads

667 since deposited on 21 Feb 2019
158 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item