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Identifying relevant compounds in patents

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  As you may know, patents can be inherently noisy documents which can make it challenging to extract drug discovery information from them, such as the key targets or compounds being claimed. There are many reasons for this, ranging from deliberate obfuscation through to the long and detailed nature of the documents. For example, a typical small molecule patent may contain extensive background information relating to the target biology and disease area, chemical synthesis information, biological assay protocols and pharmacological measurements (which may refer to endogenous substances, existing therapies, reaction intermediates, reagents and reference compounds), in addition to description of the claimed compounds themselves.  The SureChEMBL system extracts this chemical information from patent documents through recognition of chemical names, conversion of images and extraction of attached files, and allows patents to be searched for chemical structures of interest. However, the curren
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Julia meets RDKit

By Eloy
Julia is a young programming language that is getting some traction in the scientific community. It is a dynamically typed, memory safe and high performance JIT compiled language that was designed to replace languages such as Matlab, R and Python. We've been keeping an an eye on it for a while but we were missing something... yes, RDKit! Fortunately, Greg very recently added the MinimalLib CFFI interface to the RDKit repertoire. This is nothing else than a C API that makes it very easy to call RDKit from almost any programming language. More information about the MinimalLib is available directly from the source . The existence of this MinimalLib CFFI interface meant that we no longer had an excuse to not give it a go! First, we added a BinaryBuilder recipe for building RDKit's MinimalLib into Julia's Yggdrasil repository (thanks Mosè for reviewing!). The recipe builds and automatically uploads the library to Julia's general package registry. The build currently targe
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Target predictions in the browser with RDKit MinimalLib (JS) and ONNX.js

By Eloy
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
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Drug safety information: Boxed warnings and Withdrawn drugs

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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
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Mapping lists of IDs in ChEMBL

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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
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ChEMBL 28 Released!

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  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
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Sequence similarity searches in ChEMBL

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  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
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