PaccMann

Prediction of anticancer compound sensitivity with
multi-modal attention-based neural networks

We integrate three key pillars of drug sensitivity:

1 The molecular structure of compounds

2 Transcriptomic profiles of cancer cells

3 Prior knowledge about interactions among proteins within cells

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.

References

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.

PaccMann schematic

Predict anti-cancer compound sensitivity.

paccmann architecture
paccmann architecture
paccmann architecture
paccmann architecture

Ask the experts

Jannis Born

Jannis Born
Graduate student

Joris Cadow

Joris Cadow
IBM Data scientist

Matteo Manica

Matteo Manica
IBM Research scientist

Ali Oskooei

Ali Oskooei
Post-doctoral researcher