The ChEMBL-og

The Open PHACTS Discovery Platform is a freely accessible infrastructure that semantically integrates publicly available data for applied life science R&D. The Platform provides a powerful Application Programming Interface (API) which allows application builders and researchers to query the integrated data using existing applications, to build new applications and to access the API using workflows tools (e.g. KNIME and Pipeline Pilot). Examples of such applications, which illustrate what can be achieved, include the Open PHACTS Explorer, ChemBioNavigator, and PharmaTrek. The Open PHACTS Community Workshop in London on Thursday 26th June aims to introduce members of the academic community to the Open PHACTS Discovery Platform. The workshop will be of interest to: ·         Researchers who would benefit from directly querying the Open PHACTS API using scripting languages or by developing applications to consume the data. ·...

EuroQSAR-2014 will be held in St.-Petersburg, Russia on August 31st - September 4th, 2014 . The deadline for oral talks' abstracts submission to the EuroQSAR-2014 is April 23rd, 2014. The meeting, entitled Understanding Chemical-Biological Interactions, will include 9 plenary lectures and 28 oral communications, which will be selected from the submitted abstracts and will focus on: Chemical-Biological Space: Representation, Visualisation and Navigation. Chemo- and Bioinformatics Approaches to Multi-Target (Q)SAR. Modeling of Protein-Ligand Interactions: Structure, Function and Dynamics. Assessing Ligand Binding Kinetics. Computational Toxicology in Drug and Chemical Safety Assessment. Translational Bioinformatics: From Genomes to Drugs. Emerging QSAR and Modeling Methods. Two seminars/roundtables are also planned on the last day of the Symposium: (Q)SAR-Related European Initiatives. Employing Proper Statistical Approaches for QSAR Modeling and Best Publishing ...

As promised in the previous post , the ChEMBL target prediction models are now available to download from here . Furthermore, here is an IPython Notebook that showcases how the models can be used in Python. As usual, your feedback is very welcome.  George

We have just had a paper accepted in PLoS Computational Biology on the work we've done on allosteric modulators (first mentioned on the blog  here ).  The work is based on the mining of allosteric bioactivity points from ChEMBL_14. The data set of allosteric and non-allosteric interactions is available on our FTP site ( here ). This blogpost will just highlight some sections of the paper, but we would like to refer the interested reader to the full paper ( here ).  Dataset The dataset contains ChEMBL annotated and cleaned data divided in both an 'allosteric' set and a 'non-allosteric' (or background) set. Abstracts and titles mentioning allosteric keywords were pulled and from the resulting papers we extracted the primary target and all bioactivities on this primary target. From the remainder of the papers we also retrieved the primary target and all bioactivities on this primary target in a similar manner.  Targets When we observed the target distr...

In case you haven't noticed, ChEMBL_18 has arrived. As usual, it brings new additions, improvements and enhancements both on the data/annotation, as well as on the interface. One of the new features is the target predictions for small molecule drugs. If you go to the compound report card for such a drug, say imatinib  or cabozantinib , and scroll down towards the bottom of the page, you'll see two tables with predicted single-protein targets, corresponding to the two models that we used for the predictions.   - So what are these models and how were they generated?  They belong to the family of the so-called ligand-based target prediction methods. That means that the models are trained using ligand information only. Specifically, the model learns what substructural features (encoded as fingerprints) of ligands correlate with activity against a certain target and assign a score to each of these features. Given a new molecule with a new set of feature...

We are pleased to announce the release of ChEMBL_18. This version of the database was prepared on 12th March 2014 and contains: 1,566,466 compound records 1,359,508 compounds (of which 1,352,681 have mol files) 12,419,715 activities 1,042,374 assays 9,414 targets 53,298 documents The web front end at https://www.ebi.ac.uk/chembl is now connected to the ChEMBL 18 data, but you can also download the data from the ChEMBL ftpsite . Please see ChEMBL_18 release notes for full details of all changes in this release. Changes since the last release   New data sets   The ChEMBL_18 release includes the following new datasets: University of Vienna G-glycoprotein (pgp) screening data UCSF MMV Malaria Box screening data DNDi Trypanosoma cruzi screening data DrugMatrix in vivo toxicology data In addition, 43,335 new compound records from 2015 publications in the primary literature have been added to this release. Approved drug and usan data have also been...