Where life science
Collaborates
We are a global, not-for-profit members’ organization working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration.
Three-day EU conference covering range of topics examines how...
Project backed by major pharma will advance the Lab...
Participants used the AI environment created by Elsevier’s Entellect...
Our projects transform R&D innovation through pre-competitive collaboration. We bring together the key constituents to identify the root causes that lead to R&D inefficiencies. We develop best practices and technology pilots to overcome common obstacles.
Method recapitulation is difficult and time consuming, within a company and even more across collaborating partners. A solution to store, search and retrieve a digital version of a method will improve scientist’s ability to retain institutional knowledge, reduce time for method development and improve method execution
Reproducibility of methods is expected to be significantly improved while time and resources to re-establish a method in a different lab will be drastically reduced.
From a cyber security perspective, a centralized storage location will create a much more resilient environment.
The project will achieve:
The transformation of text-based method descriptions into digital instruction sets for different analytical methods and instrument types, including a common digital format and an ontology for analytical chemistry methods.
The HPLC experts have identified a common set of HPLC parameters across different CDS and HPLC’s.
Semantic experts have built the common parameters into a semantic Data Model. The Allotrope Data Format is used to bring semantic description, result data and files together in one universal HDF5 container.
A notation plus open source editors and tools to use it
The increase in sophistication of biotherapeutics research has revealed a gap in informatics methodology whereby small-molecule technologies are too unwieldy for representing large molecules, and sequence-based technologies are incapable of representing the growing variety of biomolecular modalities that include modifications such as unnatural monomers (amino acids, nucleotides) and other chemical modifications (e.g. bioconjugates).
The project has published the HELM notation and created a suite of open-source tools all free to all: a toolkit, a webeditor and an antibody editor.
There has been wide adoption and HELM is included in the technical guidance for ISO 11238 TS 19844.
We maintain a community to tackle new challenges that arise as HELM expands its reach. We are currently developing our documentation of best practices, extending our guidance around monomer sets and ensuring all adopters are supported.
The aim of the Unified Data Model (UDM) project is to create and publish an open and freely available data format for storage and exchange of experimental information about compound synthesis and biological testing.
Without UDM there is a lack of consistency in data formats coming from different systems and this makes it difficult to share experimental information. At best, time and resource is taken up trying to interpret different data formats, at worst valuable data is ignored because it can’t be shared and understood.
We will create and publish an open and freely available data format for storage and exchange of experimental information about compound synthesis and biological testing. We aim to make UDM an industry-wide data standard used to facilitate data sharing and collaboration.
Collaboration across vendors and Pharma customers has been the key to building a set of requirements that have driven the development of UDM. The project started with the file format used by Elsevier to upload chemical reaction data into Reaxys, through listening to representation of the community at the project team, requirements were gathered and the format built .
