  The understanding of the type of inhibitory interaction plays an important
role in drug design. Therefore, researchers are interested to know whether a
drug has competitive or non-competitive interaction to particular protein
targets.
  Method: to analyze the interaction types we propose factorization method
Macau which allows us to combine different measurement types into a single
tensor together with proteins and compounds. The compounds are characterized by
high dimensional 2D ECFP fingerprints. The novelty of the proposed method is
that using a specially designed noise injection MCMC sampler it can incorporate
high dimensional side information, i.e., millions of unique 2D ECFP compound
features, even for large scale datasets of millions of compounds. Without the
side information, in this case, the tensor factorization would be practically
futile.
  Results: using public IC50 and Ki data from ChEMBL we trained a model from
where we can identify the latent subspace separating the two measurement types
(IC50 and Ki). The results suggest the proposed method can detect the
competitive inhibitory activity between compounds and proteins.
