  Toxicity analysis and prediction are of paramount importance to human health
and environmental protection. Existing computational methods are built from a
wide variety of descriptors and regressors, which makes their performance
analysis difficult. For example, deep neural network (DNN), a successful
approach in many occasions, acts like a black box and offers little conceptual
elegance or physical understanding. The present work constructs a common set of
microscopic descriptors based on established physical models for charges,
surface areas and free energies to assess the performance of multi-task
convolutional neural network (MT-CNN) architectures and a few other approaches,
including random forest (RF) and gradient boosting decision tree (GBDT), on an
equal footing. Comparison is also given to convolutional neural network (CNN)
and non-convolutional deep neural network (DNN) algorithms. Four benchmark
toxicity data sets (i.e., endpoints) are used to evaluate various approaches.
Extensive numerical studies indicate that the present MT-CNN architecture is
able to outperform the state-of-the-art methods.
