Below you will find some resources on drugs, drug effects, pharmacological pathways, and genetic interactions. All are free and open for academic use (and for most other uses too). Please acknowledge and cite our work. If you have any questions please do not hesitate to contact us.

Family Relationship and Disease Data

De-identified family data on over 3,000 conditions at two sites. Data are from approximately 1.5 million patients across the two sites and all identifying information has been removed. Further, ages have been replaced with a random poisson distribution with lambda set to the actual age of the patient. Data are compatible with the observation heritability estimation software (

If you would like the 500 significant traits as reported in Polubriaginof, et al. in Cell, go to this page.

All code to generate the relationships from hospital data is publicly available in our RIFTEHR github.

Cite this resource as

Polubriaginof, Fernanda C. G. et al. “Disease Heritability Inferred from Familial Relationships Reported in Medical Records.” Cell 173.7 (2018): 1692–1704.e11. PMC. Web. 10 Oct. 2018.


Downstream effects of targeted proteins is essential to drug design. We introduce a data-driven method named DATE, which integrates drug-target relationships with gene expression, protein-protein interaction, and pathway annotation data to connect Drugs to target pAthways by the Tissue Expression. Links drugs to tissue-specific target pathways.

467,396 connections for 1,034 drugs and 954 pathways in 259 tissues/cell lines available.

Cite this resource as

Hao, Yun et al. “Tissue‐Specific Analysis of Pharmacological Pathways.” CPT: Pharmacometrics & Systems Pharmacology 7.7 (2018): 453–463. PMC. Web. 10 Oct. 2018.


G protein-coupled receptors (GPCRs) are central to how cells respond to their environment and a major class of pharmacological targets. We developed a data-driven method named GOTE, that connects Gpcrs to dOwnstream cellular pathways by the Tissue Expression. Links G-protein coupled receptors to tissue-specific molecular pathways.

93,012 connections for 213 GPCRs and 654 pathways in 196 tissues/cell types available. Code available here.

Cite this resource as

Hao, Yun, and Nicholas P. Tatonetti. “Predicting G Protein-Coupled Receptor Downstream Signaling by Tissue Expression.” Bioinformatics 32.22 (2016): 3435–3443. PMC. Web. 10 Oct. 2018.


Network analysis framework that identifies adverse event (AE) neighborhoods within the human interactome (protein-protein interaction network). Drugs targeting proteins within this neighborhood are predicted to be involved in mediating the AE. Links drugs to seed sets of proteins and phenotypes, like drug side-effects and diseases.

A description of the algorithm is available here. Code in Python available on GitHub.

Cite this resource as

Lorberbaum, Tal et al. “Systems Pharmacology Augments Drug Safety Surveillance.” Clinical pharmacology and therapeutics 97.2 (2015): 151–158. PMC. Web. 10 Oct. 2018.


The world’s first comprehensive knowledge base for therapeutic uses of venoms. As of its original release, contains 39,000 mined from MEDLINE describing potentially therapeutic effects of venoms on the human body. Links venom compounds to physiological effects. 

39K venom/effect associations in three databases available for download. Code available on GitHub.

Cite this resource as

Romano, Joseph D., and Nicholas P. Tatonetti. “VenomKB, a New Knowledge Base for Facilitating the Validation of Putative Venom Therapies.” Scientific Data 2 (2015): 150065. PMC. Web. 10 Oct. 2018.


Interspecies, network-based predictions of synthetic lethality and the first genome-wide scale prediction of synthetic lethality in humans. Scores were validated against three independent databases of synthetic lethal pairs in humans, mouse, and yeast. The original release contains ~109 million gene pairs with their associated synthetic lethality scores.

Human synthetic lethal gene pairs available in 3 parts: part 1, part 2, and part 3. And mouse too.

Cite this resource as

Jacunski, Alexandra, Scott J. Dixon, and Nicholas P. Tatonetti. “Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.” Ed. Lilia M. Iakoucheva. PLoS Computational Biology 11.10 (2015): e1004506. PMC. Web. 10 Oct. 2018.

Offsides and Twosides

Drug side effects and drug-drug interactions were mined from publicly available data. Offsides is a database of drug side-effects that were found, but are not listed on the official FDA label. Twosides is the only comprehensive database drug-drug-effect relationships. Over 1,000 drugs and 63,000 combinations connected to millions of potential adverse reactions.

Download the data from the website. Code is available at

Cite this resource as

Tatonetti, Nicholas P. et al. “Data-Driven Prediction of Drug Effects and Interactions.” Science Translational Medicine 4.125 (2012): 125ra31. PMC. Web. 10 Oct. 2018.