Version 1
: Received: 13 April 2021 / Approved: 14 April 2021 / Online: 14 April 2021 (17:44:40 CEST)
How to cite:
Preto, A.J.; Matos-Filipe, P.; Mourão, J.; Moreira, A.I.S. SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning. Preprints2021, 2021040395. https://doi.org/10.20944/preprints202104.0395.v1
Preto, A.J.; Matos-Filipe, P.; Mourão, J.; Moreira, A.I.S. SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning. Preprints 2021, 2021040395. https://doi.org/10.20944/preprints202104.0395.v1
Preto, A.J.; Matos-Filipe, P.; Mourão, J.; Moreira, A.I.S. SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning. Preprints2021, 2021040395. https://doi.org/10.20944/preprints202104.0395.v1
APA Style
Preto, A.J., Matos-Filipe, P., Mourão, J., & Moreira, A.I.S. (2021). SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning. Preprints. https://doi.org/10.20944/preprints202104.0395.v1
Chicago/Turabian Style
Preto, A.J., Joana Mourão and and Irina S. Moreira. 2021 "SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning" Preprints. https://doi.org/10.20944/preprints202104.0395.v1
Abstract
High-throughput screening technologies continues to produce large amounts of multiomics data from different populations and cell types for various diseases, such as cancer. However, analysis of such data encounters difficulties due to cancer heterogeneity, further exacerbated by human biological complexity and genomic variability. There is a need to redefine the drug discovery development pipeline, bringing an Artificial Intelligence (AI)-powered informational view that integrates relevant biological information and explores new ways to develop effective anticancer approaches. Here, we show SynPred, an interdisciplinary approach that leverages specifically designed ensembles of AI-algorithms, links omics and biophysical traits to predict synergistic anticancer drug synergy. SynPred exhibits state-of-the-art performance metrics: accuracy – 0.85, precision – 0.77, recall – 0.75, AUROC – 0.82, and F1-score - 0.76 in an independent test set. Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application available online at http://www.moreiralab.com/resources/synpred/ was constructed to predict synergistic anticancer drug combinations requiring only the upload of the two drug SMILES to be tested, allowing easy access by non-expert researchers.
Keywords
Deep-Learning; Interpretability; Omics; Biophysics; Drug Synergy; Cancer
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
