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

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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.

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