ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
By Mikhail Zaslavskiy, Simon Jégou, Eric W. Tramel, Gilles Wainrib. 2018
26 citations recorded by the source corpus.
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 this http URL.
Subfields: Analytical Chemistry, Computational Theory and Mathematics
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In Computational Theory and Mathematics
- Artem Cherkasov
- Katsuhiko Ishiguro
- Zaccary Alperstein
- Masanori Koyama
- Jason Tyler Rolfe
- Shin-ichi Maeda
Citations in the corpus
- Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks (2019, methodology): “A more recent paper [33] proposes an ensemble method of GNNs and gradient boosts, and records the scaffold AUC score of 0 .”
- All SMILES Variational Autoencoder (2019, methodology): “(a) ZINC250k M ODEL MAE LOG P MAE QED ECFP [53] 0.38 0.045 CVAE [16] 0.15 0.054 CVAE ENC [16] 0.13 0.037 G RAPH C ONV [12] [38] 0.854 P OTENTIAL N ET [14] 0.857 ± 0.006 T OXIC B LEND [71] 0.862 All SMILES 0.871”