  Although artificial neural networks have occasionally been used for
Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in
the past, the literature has of late been dominated by other machine learning
techniques such as random forests. However, a variety of new neural net
techniques along with successful applications in other domains have renewed
interest in network approaches. In this work, inspired by the winning team's
use of neural networks in a recent QSAR competition, we used an artificial
neural network to learn a function that predicts activities of compounds for
multiple assays at the same time. We conducted experiments leveraging recent
methods for dealing with overfitting in neural networks as well as other tricks
from the neural networks literature. We compared our methods to alternative
methods reported to perform well on these tasks and found that our neural net
methods provided superior performance.
