Open Source Tools

Our team on GitHub

Therapeutics Data Commons

Machine Learning Datasets and Tasks for Therapeutics

View TDC TDC Website


Graph meta learning via local subgraphs

View G-Meta G-Meta Project Website


Subgraph Neural Networks

View SubGNN SubGNN Project Website


Defending graph neural networks against adversarial attacks

View GNNGuard GNNGuard Project Website

Graph ML Tutorials

Tutorials on machine learning for graphs

View Graph ML Tutorials


Python module for fast non-negative matrix factorization

View Nimfa Nimfa Project Website


Graph neural networks for multirelational link prediction

View Decagon


Deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening

View DeepPurpose


Skip-graph networks for molecular interaction prediction

View SkipGNN


Data fusion via collective latent factor models

View Scikit-fusion

Network Enhancement

Method for denoising biological networks

View NE


Method for prioritizing network communities

View CRank


Representation learning for multi-layer graphs

View OhmNet


Tool for construction, representation, and analysis of large multi-modal networks

View Mambo


Method for generating explanations for graph neural networks

View GNNExplainer


Method for learning structural node embeddings

View GraphWave

Graph Query Embeddings

Method for embedding logical queries on knowledge graphs

View Graph Query Embeddings


Method for gene prioritization by compressive data fusion and chaining

View Collage

Network-Guided Matrix Completion

Method for probabilistic prediction and imputation of interactions using prior knowledge



Fast methods for non-negative matrix tri-factorization

View Fast-NMTF


Deep multi-task learning for cross-type biomedical named entity recognition

View Multi-BioNER


Scalable multi-GPU and multi-CPU methods for non-negative matrix tri-factorization


Latest News

Jul 2021:   Best Paper Award at ICML Interpretable ML for Healthcare

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at ICML 2021 Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.

Jul 2021:   Five presentations at ICML 2021

Jun 2021:   Theory and Evaluation for Explanations

We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.

Jun 2021:   Deep Contextual Learners for Protein Networks

New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.

May 2021:   New Paper Accepted at UAI

Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.

Apr 2021:   Hot Off the Press: COVID-19 Repurposing in PNAS

Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Mar 2021:   Fair and Stable Graph Representation Learning

We are thrilled to share the latest preprint on fair and stable graph representation learning.

Feb 2021:   New Preprint on Therapeutics Data Commons

Jan 2021:   An Algorithmic Approach to Patient Safety

The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.

Jan 2021:   Workshop on AI in Health at the Web Conference

We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.

Jan 2021:   Tutorial on ML for Drug Development

We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

Dec 2020:   Two New Papers Published

Dec 2020:   Bayer Early Excellence in Science Award

Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!

Nov 2020:   Therapeutics Data Commons (TDC)

We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.

Nov 2020:   National Symposium on the Future of Drugs

On behalf of the NSF, we are organizing the National Symposium on Drug Repurposing for Future Pandemics. We have a stellar lineup of invited speakers! Register at

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics