MedChemica release first results of Multi-Pharma Knowledge Sharing Consortium - Read this article on LinkedIn and SlideShare  

Available on Slideshare our talk at the American Chemistry Society meeting Fall 2016 MedChemica - Extracting and exploiting medicinal chemistry ADMET knowledge automatically from public and large pharma data  

We are happy to announce that MedChemica is a partner of Horizon2020 Marie Skodowska-Curie ITN BIGCHEM network. We are looking for 10 doctoral (PhD) positions in Big Data Analysis in Chemistry. Please apply at BigChem Website  

Available on Slideshare our Poster from Cambridge Med Chemistry conference Multi-Parameter Optimization of Pharmaceuticals: the Big-Data Way  

MedChemica is awarded a grant from the Technology Strategy Board (TSB)  

MedChemica helps the Protein Protein Interactions Network (PPI-Net) by researching and compiling a community document and new interactive network maps.The community document takes a snapshot of current capability in the UK (which is substantial) and begins to paint a road map for the future in this tough area of research. The network maps show the connectivity and collaborations between researchers.

  27th June 2013 - So our knowledge sharing design engine is called SALT MINER - why did we call it that - read the story on our blog    

We are delighted to announce the formation of a consortium consisting of AstraZeneca Plc, Hoffman La Roche, Genentech and MedChemica. Using Matched Molecular Pair Analysis will be processing data from all companies to yield the SALT Knowledge Share system to generate a Grand Rule Database of Medicinal Chemistry Knowledge. We will make this available for drug discovery

Dr Ed Griffen presents Knowledge Based Design using Matched Molecular Pair Analysis for Multiple Parallel Optimisation in Drug Discovery at both European and Japanese OpenEye User Group meetings in June 2013. His slide set can be found here on slideshare  

MedChemica are delighted to announce the online publication of "Matched Molecular Pair Analysis in Drug Discovery". Abstract - Multiple parameter optimisation in drug discovery is difficult, but Matched Molecular Pair Analysis (MMPA) can help. Computer algorithms can process data in an unbiased way to yield design rules and suggest better molecules, cutting the number of