Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders

• Verfasst von: Manica, Matteo [VerfasserIn]
• Verfasst von: Oskooei, Ali [VerfasserIn]
• Verfasst von: Born, Jannis [VerfasserIn]
• Verfasst von: Subramanian, Vigneshwari [VerfasserIn]
• Verfasst von: Sáez Rodríguez, Julio [VerfasserIn]
• Verfasst von: Rodríguez Martínez, María [VerfasserIn]
• Titel: Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders
• Verf.angabe: Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, and María Rodríguez Martínez
• E-Jahr: 2019
• Jahr: October 16, 2019
• Umfang: 10 S.
• Fussnoten: Gesehen am 17.01.2020
• Titel Quelle: Enthalten in: Molecular pharmaceutics
• Ort Quelle: Washington, DC : American Chemical Society, 2004
• Jahr Quelle: 2019
• Band/Heft Quelle: 16(2019), 12, Seite 4797-4806
• ISSN Quelle: 1543-8392
• DOI: doi:10.1021/acs.molpharmaceut.9b00520
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1687790361

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 encoder significantly outperforms a baseline model trained on Morgan fingerprints and a selection of encoders based on SMILES, as well as the 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 to 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.