Newsflash
SciWalker Open Data @ACS San Diego
We just presented our new SciWalker Open Data at ACS San Diego. SciWalker is a new open access semantic search engine for life science data. Try it yourself at www.sciwalker.com.
Developing novel anticancer therapies
Korean investors from PAGS and OntoChem sign a termsheet to jointly develop novel anticancer therapies.
New Publication
New chemical compounds usually find their first public mention in patents. Patent publications in the chemical domain usually are full of chemical expressions, making it difficult to extract those compounds that are really of interest. We have developed a system that is able not only to recognize chemical compounds in patent texts but also to decide to which degree they have relevance. https://lnkd.in/dfQCHri
InChI Trust Membership
Continuing our long term efforts to make chemical information available to everyone, the OntoChem IT Solutions is proud to be able to join the InChI Trust. The InChI Trust is the agency behind the internationally agreed standard representation of chemical structures, the IUPAC InChI. Those chemical identifiers have been widely used throughout our chemical ontologies for years and we are looking forward to continue this standardization drive together with the other InChI Trust members and associates.
OIS and novomol develop new compound property prediction systems
OIS and novomol (www.novomol.com) collaborate to jointly develop new Big Data analytics solutions in the field of compound properties. OntoChem IT Solutions is extracting compound properties from its large scale data collections such as patents, scientific articles, or webpages. Novomol is then using its novel proprietary EDEN platform to analyze the data and create models for compound property prediction systems.
The new system will allow to predict molecular properties such as biological activity against a panel of targets, physico-chemical properties such as solubility as well as ADMET properties such as toxicities.
Different to known systems, the EDEN approach leverages the Big Data space on molecules as extracted from scientific documents, predicting all possible properties in one prediction run.