PaccMann is a novel approach to predict anticancer compound sensitivity by means of multi-modal attention-based neural networks.
Our model ingests a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value. Gene expression profiles are encoded using an attention-based encoding mechanism that assigns high weights to the most informative genes.
SMILES are encoded using an attention-based encoder that highlights the most relevant structural features of the compound.
Thanks to these encoders, PaccMann outperforms deep-learning models that use engineered fingerprints.
Furthermore, the adoption of attention-based encoders enhance interpretability and enable us to identify genes, bonds and atoms that are used by the network to make a prediction, providing useful insights into both drug discovery and precision medicine settings.
“Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders,”
Matteo Manica et al., Workshop on Computational Biology ICML, arXiv:1904.11223, 2019.
“PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks,”
Ali Oskooei et al., Workshop on Machine Learning for Molecule and Materials NeurIPS, 2018.