A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

We present MuSSeL,
a multifingerprint similarity search algorithm,
able to predict putative drug targets for a given query small molecule
as well as to return a quantitative assessment of its bioactivity
in terms of Ki or IC50 values. Predictions are automatically made exploiting a
large collection of high quality experimental bioactivity data available
from ChEMBL (version 22.1) combining, in a consensus-like approach,
predictions resulting from a similarity search performed using 13
different fingerprint definitions. Importantly, the herein proposed
algorithm is also effective in detecting and handling activity cliffs.
A calibration set including small molecules present in the last updated
version of ChEMBL (version 23) was employed to properly tune the algorithm
parameters. Three randomly built external sets were instead challenged
for model performances. The potential use of MuSSeL was also challenged
by a prospective exercise for the prediction of five bioactive compounds
taken from articles published in the Journal of Medicinal Chemistry
just few months ago. The paper emphasizes the importance of implementing
multifingerprint consensus strategies to increase the confidence in
prediction of similarity search algorithms and provides a fast and
easy-to-run tool for drug target and bioactivity prediction.