The ChEMBL-og

Have you ever wondered which compound is the most popular in ChEMBL? And by popular I don't mean the one which cracks the best jokes at dinner parties; I mean the compound with the largest number of structural analogues or nearest neighbours (NNs). This number also gives an indication of the sparsity or density of the chemical space around a compound and is a useful concept during hit expansion and lead optimisation.  This number of course depends on the fingerprint, the hashing and folding parameters, the similarity coefficient and the threshold. So let's say 2048-bit RDKit Morgan fingerprints with a radius of 2 or 3 (equivalent to ECFP_4 or ECFP_6) and Tanimoto threshold of 0.5. Why so low threshold? For an explanation, see here and here . To calculate this compound 'popularity', one would need to calculate the full similarity matrix of the 1.4M compounds in ChEMBL. This used to be prohibitively computationally expensive just a few years ago; nowadays,...

I’ve been asked a lot by mail recently ‘What’s Going On?’ Well, here is are some facts and some emotion. So today is my last day at work here at EMBL-EBI. It’s been a fun and thrilling ride (for me at least), I’ve made lots of new friends, living life as an Open Data advocate and academic researcher, and most importantly having the privilege to lead the team here responsible for the ChEMBL database. It had been a long-term goal of mine to unlock large-scale bioactivity data from proprietary data silos and eye-wateringly expensive paywalls; so as US President George Bush famously said ‘Mission Accomplished!’. The impact of ChEMBL on academia, SMEs and large pharma has been great - and you can see the impact in new method development, but more importantly in potential new future drugs. My personal indebtedness to the Wellcome Trust for their support is immeasurable. An additional big shout out to Digital Science for their vision in  donating  the SureChEMBL platform to...