Marinka Zitnik

About

Marinka Zitnik is an Associate Professor in the Department of Biomedical Informatics at Harvard Medical School and an Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. She is also an Associate Member at the Broad Institute of MIT and Harvard and an Affiliated Faculty at the Harvard Data Science Initiative. Her research investigates the foundations of artificial intelligence to enhance scientific discovery and to realize individualized diagnosis and treatment. Her lab aims to lay the foundations for AI that contribute to the scientific understanding of therapeutic design and genomic medicine or acquire such understanding autonomously. Her work focuses on using AI to describe the state of a person with increasing precision by incorporating modalities such as genetic code, cellular atlases, molecular datasets, and therapeutics. The challenge her research addresses is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Her lab creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time, ensuring medicinal effects are consistent across individuals and laboratory results. Additionally, her research seeks to change the traditional method of scientific discovery by using AI to disentangle the complexity of interconnected biological systems, advancing drug design and developing new therapies. Dr.…

Research topics

• Data Mining
• Computer Science
• Artificial Intelligence
• Machine Learning
• Biology
• Cognitive science
• Bioinformatics
• Programming language
• Data science

Selected publications

• Scientific discovery in the age of artificial intelligence

  Nature · 2023 · 1538 citations

  Senior authorCorresponding
  PublisherDOI

• Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

  PubMed Central · 2020-04-15 · 528 citations

  articleOpen access

  The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screene…

  PublisherDOI

• DeepPurpose: a deep learning library for drug–target interaction prediction

  Bioinformatics · 2020 · 462 citations

  SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implemen…

  DOI

• Quantum-machine-assisted drug discovery

  npj Drug Discovery. · 2026-01-07 · 5 citations

  preprintOpen access

  Drug discovery is lengthy and expensive, with traditional computer-aided design facing limits. This paper examines integrating quantum computing across the drug development cycle to accelerate and enhance workflows and rigorous decision-making. It highlights quantum approaches for molecular simulation, drug-target interaction prediction, and optimizing clinical trials. Leveraging quantum capabilities could accelerate timelines and costs for bringing therapies to market, improving efficiency and…

  PublisherOA PDFDOI

• Democratizing AI scientists using ToolUniverse

  ArXiv.org · 2025-09-27 · 5 citations

  preprintOpen accessSenior author

  AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid workflows, and lack shared environments that unify tools, data, and analyses into a common ecosystem. In genomics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development; AI scientists require comparable infrastructure. We present ToolUniverse, an ecosystem fo…

  PublisherOA PDFDOI

Recent grants

• RAPID:Collaborative Research: Computational Drug Repurposing for COVID-19

  NSF · $100k · 2020–2021

• Workshop on Drug Repurposing for Future Pandemics

  NSF · $30k · 2020–2020

Frequent coauthors

• Jure Leskovec

  Stanford University

  64 shared

• Blaž Zupan

  Baylor College of Medicine

  59 shared

• Xiang Zhang

  Air Force Medical University

  52 shared

• Kexin Huang

  41 shared

• Xiang Zhang

  28 shared

• William C. Hahn

  Dana-Farber Cancer Institute

  22 shared

• Theodoros Tsiligkaridis

  21 shared

• Payal Chandak

  Harvard–MIT Division of Health Sciences and Technology

  21 shared

Labs

• Zitnik LabPI

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