OnSIDES, side effects extracted from FDA Structured Product Labels

OnSIDES (ON label SIDE effectS resource) is the newest member of the NSIDES family. The initial release (v01) of the OnSIDES database of adverse reactions and boxed warnings extracted from the FDA structured product labels. All labels available to download from DailyMed (https://dailymed.nlm.nih.gov/dailymed/spl-resources-all-drug-labels.cfm) as of April 2022 were processed in this analysis. In total 2.7 […]


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 3,300 drugs and 63,000 combinations connected to millions of potential adverse reactions. Read more and access […]

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 […]


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 […]


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 […]


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 […]


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.


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 […]