3DSTarPred: A Web Server for Target Prediction of Bioactive Small Molecules Based on 3D Shape Similarity

Target identification plays a critical
role in preclinical
drug
development. The in silico approach has been developed
and
widely applied to assist medicinal chemists and pharmacologists in
drug target identification. There are many target prediction web servers
available today that have revealed both advantages and shortcomings
in practical applications. Here, we present 3DSTarPred, a web server
for three-dimensional (3D) shape similarity-based target prediction
of small molecules. A benchmark study showed that 3DSTarPred achieved
a target prediction success rate of 76.27%, which was higher than
that of existing target prediction web servers. In addition, the performance
of 3DSTarPred in the target prediction of diverse substructures/superstructures
was also better than that of the existing target prediction web servers.
In case studies, 3DSTarPred was used to identify the potential targets
of two small molecules, one being kaempferol, a natural lead compound
for the treatment of Alzheimer’s disease (AD), and the other
being sildenafil, a candidate for drug repurposing in AD. The case
studies further demonstrated the reliability and success of 3DSTarPred
in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.