The ChEMBL-og

Some time ago we showed an example of how a model trained in Python's PyTorch could be run in a C++ backend by exporting it to the ONNX format.  Greg also showed us in his blogpost how our multitask neural network model could be used in a very nice KNIME workflow by exporting it to ONNX. That was possible thanks to RDKit's Java bindings and the ONNX Java runtime. As a refresher, most of the most popular machine learning frameworks can export their models to this format and many programming languages can load them to run the predictions. This certainly is a beautiful example of interoperability! In November 2019 RDKit introduced a reduced functionality Javascript library which is able to do all we need in order to use our multitask model in the browser. So, the only thing that was left to do was to combine these two awesome tools... and we did it! Here is our demo with its available source code . Start typing a smiles into the box and enjoy! Updated code to generate the m

Updated drug safety information is available (as of ChEMBL 28 ) for drugs with boxed warnings and for withdrawn drugs.  Boxed warnings (also know as black box warnings) are provided on medicinal product labels for FDA approved drugs if the medicinal product can cause severe or life-threatening side effects. They are free text descriptions, enclosed within a black box, hence the name! For example,  Oxaprozin  is used to treat osteoarthritis but carries a boxed warning. Our recent work has classified the type of adverse effect described in boxed warnings on a per drug basis. For medicinal products that contain one active pharmaceutical ingredient, a boxed warning can be directly linked to a drug. Therefore, toxicity class(es) have been assigned to approved drugs with boxed warning information described on medicinal product labels (e.g. Cardiotoxicity, Hepatotoxicity etc). Clickable links to examples of medicinal product labels with boxed warning text descriptions have been retained to al

In order to facilitate the mapping of identifiers in ChEMBL, we have developed a new type of search in the ChEMBL Interface. Now, it is possible to enter a list of ChEMBL IDs and see a list of the corresponding entities. Here is an example: 1. Open the ChEMBL Interface , on the main search bar, click on 'Advanced Search': 2. Click on the 'Search by IDs' tab: 3. Select the source entity of the IDs and the destination entity that you want to map to: 4. Enter the identifiers, you can either paste them, or select a file to upload. When you paste IDs, by default it tries to detect the separator. You can also select from a list of separators to force a specific one: Alternatively, you can upload a file, the file can be compressed in GZIP and ZIP formats, this makes the transfer of the file to the ChEMBL servers faster. Examples of the files that can be uploaded to the search by IDs can be found  here . 5. Click on the search button: 6. You will be redirected to a search resul

  We are pleased to announce the release of ChEMBL_28. This version of the database, prepared on 15/01/2021 contains: * 2,680,904 compound records * 2,086,898 compounds (of which 2,066,376 have mol files) * 17,276,334 activities * 1,358,549 assays * 14,347 targets * 80,480 documents Data can be downloaded from the ChEMBL FTP site:   https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_28 . Please see ChEMBL_26 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_28/chembl_28_release_notes.txt DATA CHANGES SINCE THE LAST RELEASE This release includes several new deposited data sets: Donated Chemical Probes data from SGC Frankfurt (src_id = 54) SARS-CoV-2 screening data from the Fraunhofer Institute (src_id = 52) Antimicrobial screening data sets from CO-ADD (src_id = 40) Plasmodium screening data from the UCSD Winzeler lab (src_id = 51) MMV pathogen box screening data (src_id = 34) Curated data

  The ChEMBL database contains bioactivity data that links compounds to their biological targets.  Most ChEMBL targets are proteins (~ 70% in version 27) and these are mapped to their UniProt accessions.   On the ChEMBL interface, searches can be performed with either protein names or accessions...but did you know that protein similarity searches are also possible? Here’s an example using human Phospholipase DDHD2 , a target not found in ChEMBL.       1.       On the ChEMBL interface , click 'Enter a Sequence:     2.       Input the FASTA sequence corresponding to human  Phospholipase DDHD2  and click 'Search in ChEMBL':  3.      Review the BLAST results, select targets of interest and browse bioactivity data: The BLAST  search identifies the mouse  Phospholipase DDHD2   homologue alongside a small number of bioactivity data points and active compounds . ChEMBL's sequence search feature is currently only available through the interface. However, sequence data for prote

  ChEMBL contains a broad range of binding, functional and ADMET type assays in formats ranging from  in vitro single protein assays to anti proliferative cell-based assays.  Some variation is expected, even for very similar assays, since these are often performed by different groups and institutes.  ChEMBL includes references for all bioactivity values so that full assay details can be reviewed if needed, however there are a number of other data checks that can be used to identify potentially problematic results. 1) Data validity comments: The data validity column was first included in ChEMBL v15 and flags activities with potential validity issues such as a non-standard unit for type or activities outside of the expected range. Users can review flagged activities and decide how these should be handled. The data validity column can be viewed on the interface (click 'Show/Hide columns' and select 'data validity comments') and can be found in the activities table in the