Machine learning for drug development with Marinka Zitnik

In this episode, Jacob Schreiber interviews Marinka Zitnik 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, Modeling polypharmacy side effects with graph

convolutional networks.

Links:

Modeling polypharmacy side effects with graph convolutional networks (Marinka Zitnik, Monica Agrawal, Jure Leskovec)

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19 (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)

AI Cures initiative

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