  Timely assessment of compound toxicity is one of the biggest challenges
facing the pharmaceutical industry today. A significant proportion of compounds
identified as potential leads are ultimately discarded due to the toxicity they
induce. In this paper, we propose a novel machine learning approach for the
prediction of molecular activity on ToxCast targets. We combine extreme
gradient boosting with fully-connected and graph-convolutional neural network
architectures trained on QSAR physical molecular property descriptors, PubChem
molecular fingerprints, and SMILES sequences. Our ensemble predictor leverages
the strengths of each individual technique, significantly outperforming
existing state-of-the art models on the ToxCast and Tox21 toxicity-prediction
datasets. We provide free access to molecule toxicity prediction using our
model at http://www.owkin.com/toxicblend.
