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Timely assessment of compound toxicity is one of the biggest challengesfacing the pharmaceutical industry today. A significant proportion of compoundsidentified as potential leads are ultimately discarded due to the toxicity theyinduce. In this paper, we propose a novel machine learning approach for theprediction of molecular activity on ToxCast targets. We combine extremegradient boosting with fully-connected and graph-convolutional neural networkarchitectures trained on QSAR physical molecular property descriptors, PubChemmolecular fingerprints, and SMILES sequences. Our ensemble predictor leveragesthe strengths of each individual technique, significantly outperformingexisting state-of-the art models on the ToxCast and Tox21 toxicity-predictiondatasets. We provide free access to molecule toxicity prediction using ourmodel at http://www.owkin.com/toxicblend.

ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors