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

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Awale Mahendra, Reymond Jean-Louis,
Project Chemical Space Design of Small Molecules and Peptides
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Original article (peer-reviewed)

Journal Journal of Chemical Information and Modeling
Volume (Issue) 59(1)
Page(s) 10 - 17
Title of proceedings Journal of Chemical Information and Modeling
DOI 10.1021/acs.jcim.8b00524


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 Naıve 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