SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation

Two-component crystals such as pharmaceutical
cocrystals and salts
have been proven as an effective strategy to improve physicochemical
and biopharmaceutical properties of drugs. It is not easy to select
proper molecular combinations to form two-component crystals. The
network-based models have been successfully utilized to guide cocrystal
design. Yet, the traditional social network-derived methods based
on molecular-interaction topology information cannot directly predict
interaction partners for new chemical entities (NCEs) that have not
been observed to form two-component crystals. Herein, we proposed
an effective tool, namely substructure-molecular-interaction network-based
recommendation (SMINBR), to prioritize potential interaction partners
for NCEs. This in silico tool incorporates network
and chemoinformatics methods to bridge the gap between NCEs and known
molecular-interaction network. The high performance of 10-fold cross
validation and external validation shows the high accuracy and good
generalization capability of the model. As a case study, top 10 recommended
coformers for apatinib were all experimentally confirmed and a new
apatinib cocrystal with paradioxybenzene was obtained. The predictive
capability of the model attributes to its accordance with complementary
patterns driving the formation of intermolecular interactions. SMINBR
could automatically recommend new interaction partners for a target
molecule, and would be an effective tool to guide cocrystal design.
A free web server for SMINBR is available at http://lmmd.ecust.edu.cn/sminbr/.