Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach

We
present a new quantitative ligand-based bioactivity prediction
approach employing a multifingerprint similarity search algorithm,
enabling the polypharmacological profiling of small molecules. Quantitative
bioactivity predictions are made on the basis of the statistical distributions
of multiple Tanimoto similarity θ values, calculated through
13 different molecular fingerprints, and of the variation of the measured
biological activity, reported as ΔpIC50, for all
of the ligands sharing a given protein drug target. The application
data set comprises as much as 4241 protein drug targets as well as
418 485 ligands selected from ChEMBL (release 25) by employing
a set of well-defined filtering rules. Several large internal and
external validation studies were carried out to demonstrate the robustness
and the predictive potential of the herein proposed method. Additional
comparative studies, carried out on two freely available and well-known
ligand–target prediction platforms, demonstrated the reliability
of our proposed approach for accurate ligand–target matching.
Moreover, two applicative cases were also discussed to practically
describe how to use our predictive algorithm, which is freely available
as a user-friendly web platform. The user can screen single or multiple
queries at a time and retrieve the output as a terse html table or
as a json file including all of the information concerning the explored
similarities to obtain a deeper understanding of the results. High-throughput
virtual reverse screening campaigns, allowing for a given query compound
the quick detection of the potential drug target from a large collection
of them, can be carried out in batch on demand.