This project created an open, extendable, and freely available data format for the exchange of experimental information about compound synthesis and testing
This project enabled the digitization of analytical methods for seamless exchange between different High-Performance Liquid Chromatography (HPLC) systems and Chromatography Data Systems (CDS).
This project created and established Hierarchical Editing Language for Macromolecules (HELM) as the industry’s complex biomolecule notation of choice
This project helped the life sciences industry unlock the value of data in Electronic Lab Notebooks (ELN) using data standards and semantic enrichment
In vitro Pharmacology screening is performed extensively in the drug discovery process before IND (Investigational New Drug) applications for the best drug candidates are submitted to the FDA and regulatory bodies who assess their safety.
This collaborative project was launched to create a bottom-up qualitative Natural Language Processing (NLP) Use Case Database, to allow NLP practitioners in pharma companies to share successes and failures with their peers
Explore the use of Large Language Models for biological research, and define the best practices for doing so, using target discovery and validation as the initial use case
This project aims to convert unstructured assay protocol descriptions into a high-quality FAIR data set, and create standards for this information.
Our project aims to develop a pharmaceutical (CMC) process ontology, based on the ISA88/95 framework. This ontology will serve to standardize laboratory and plant production process recipes, and in turn, establish standardized definitions.
The IDMP Ontology (IDMP-O) is the pharmaceutical industry’s implementation layer for the ISO IDMP standards. It provides a shared, machine-readable framework for medicines data — one that goes beyond legal compliance to give organizations a directly deployable foundation for internal data management, regulatory submission, and cross-system interoperability.
The goal of the Social Media to Produce Real-World Evidence (RWE) project is to develop and publish key regulatory guidelines associated with the use of social media as real-world data (RWD) to produce RWE.
Project Charter: Ontologies proliferate. Their diversity limits semantic interoperability, increases costs, creates semantic siloes and slows the path to data centricity.