Integrating Incompatible Assay Data Sets with Deep Preference Learning

A large amount of
bioactivity assay data is already accumulated
in public databases, but the integration of these data sets for quantitative
structure–activity relationship (QSAR) studies is not straightforward
due to differences in experimental methods and settings. We present
an efficient deep-learning-based approach called Deep Preference Data
Integration (DPDI). For integrating outcome variables of different
assay types, a surrogate variable is introduced, and a neural network
is trained such that the total order induced by the surrogate variable
is maximally consistent with given data sets. In a task of predicting
efficacy of factor Xa inhibitors, DPDI successfully integrated 2959
molecules distributed in 129 assay data sets. In most of our experiments,
data integration improved prediction accuracy strongly in interpolation
and extrapolation tasks, indicating that DPDI is an effective tool
for QSAR studies.