Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

Abstract

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based (MCA) encoder significantly outperforms a baseline model trained on Morgan fingerprints, a selection of encoders based on SMILES as well as previously reported state of the art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify its potential for in-silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.

arXiv comment11 pages, 5 figures, 1 table, Workshop on Computational Biology at the International Conference on Machine Learning (ICML), Long Beach, CA, 2019

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Deep Reinforcement Learning for De-Novo Drug Design
• Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
• SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
• Application of generative autoencoder in de novo molecular design
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
• Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
• Drug response prediction by ensemble learning and drug-induced gene expression signatures
• Learning to SMILE(S)
• PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

Cited by in corpus (8)

• TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
• Transformers in Healthcare: A Survey
• T cell receptor binding prediction: A machine learning revolution
• PaccMann${}^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
• PaccMann${}^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
• Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation