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

Abstract

We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate three key pillars of drug sensitivity, namely, the molecular structure of compounds, transcriptomic profiles of cancer cells as well as prior knowledge about interactions among proteins within cells. Our models ingest 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. We present and study three encoders for SMILES string of compounds: 1) bidirectional recurrent 2) convolutional 3) attention-based encoders. We compare our devised models against a baseline model that ingests engineered fingerprints to represent the molecular structure. We demonstrate that using our attention-based encoders, we can surpass the baseline model. The use of attention-based encoders enhance interpretability and enable us to identify genes, bonds and atoms that were used by the network to make a prediction.

arXiv comment10 pages, 5 figures, 2 tables. NIPS MLMM 2018

References in corpus (4)

• Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
• Massively Multitask Networks for Drug Discovery
• SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

Cited by in corpus (2)

• Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
• Variational Autoencoder for Anti-Cancer Drug Response Prediction