  With the rapid development of high-throughput technologies, parallel
acquisition of large-scale drug-informatics data provides huge opportunities to
improve pharmaceutical research and development. One significant application is
the purpose prediction of small molecule compounds, aiming to specify
therapeutic properties of extensive purpose-unknown compounds and to repurpose
novel therapeutic properties of FDA-approved drugs. Such problem is very
challenging since compound attributes contain heterogeneous data with various
feature patterns such as drug fingerprint, drug physicochemical property, drug
perturbation gene expression. Moreover, there is complex nonlinear dependency
among heterogeneous data. In this paper, we propose a novel domain-adversarial
multi-task framework for integrating shared knowledge from multiple domains.
The framework utilizes the adversarial strategy to effectively learn target
representations and models their nonlinear dependency. Experiments on two
real-world datasets illustrate that the performance of our approach obtains an
obvious improvement over competitive baselines. The novel therapeutic
properties of purpose-unknown compounds we predicted are mostly reported or
brought to the clinics. Furthermore, our framework can integrate various
attributes beyond the three domains examined here and can be applied in the
industry for screening the purpose of huge amounts of as yet unidentified
compounds. Source codes of this paper are available on Github.
