#48 Machine learning for drug development with Marinka Zitnik

<p>In this episode, <a href="https://jmschrei.github.io/">Jacob Schreiber</a> interviews <a href="https://zitniklab.hms.harvard.edu/">Marinka Zitnik</a> about applications of machine learning to drug development. They begin their discussion with an overview of open research questions in the field, including limiting the search space of high-throughput testing methods, designing drugs entirely from scratch, predicting ways that existing drugs can be repurposed, and identifying likely side-effects of combining existing drugs in novel ways. Focusing on the last of these areas, they then discuss one of Marinka’s recent papers, <a href="https://academic.oup.com/bioinformatics/article/34/13/i457/5045770">Modeling polypharmacy side effects with graph convolutional networks</a>.</p> <p>Links:</p> <ul> <li><a href="https://academic.oup.com/bioinformatics/article/34/13/i457/5045770">Modeling polypharmacy side effects with graph convolutional networks</a> (Marinka Zitnik, Monica Agrawal, Jure Leskovec)</li> <li><a href="https://arxiv.org/abs/2004.07229">Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19</a> (Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, Albert-László Barabási)</li> <li><a href="https://www.aicures.mit.edu">AI Cures initiative</a></li> </ul>