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Pathogen data in ChEMBL

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Infectious disease is a leading cause of death globally and bioactivity data against pathogens (fungi, bacteria, viruses, and parasites) is an important category in ChEMBL, especially in light of the ongoing pandemic. In ChEMBL version 29, there are over 2 M bioactivity data points against fungal, bacterial or viral targets (for 460 K compounds) available for pathogen-related research. How can I find pathogen data? On the ChEMBL interface, the organism taxonomy is available as a filter that can be applied to bioactivity data. A sunburst visualisation of the organism taxonomy is also provided as an easy starting point to explore targets according to their taxonomy. In the full database, the organism_classification table holds the underlying data and can be used in bespoke SQL queries. For example, queries may be performed to extract high level pathogen data such as all bioactivity data for small molecules screened against bacterial targets (example below) or more specific subsets focus
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New Drug Warnings Browser

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As mentioned in the announcement post of  ChEMBL 29 , a new Drug Warnings Browser has been created. This is an updated version of the entity browsers in ChEMBL ( Compounds , Targets , Activities , etc). It contains new features that will be tried out with the Drug Warnings and will be applied to the other entities gradually. The new features of the Drug Warnings Browser are described below. More visible buttons to link to other entities This functionality is already available in the old entity browsers, but the button to use it is not easily recognised. In the new version, the buttons are more visible. By using those buttons, users can see the related activities, compounds, drugs, mechanisms of action and drug indications to the drug warnings selected. The page will take users to the corresponding entity browser with the items related to the ones selected, or to all the items in the dataset if the user didn’t select any. Additionally, the process of creating the join query is no
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ChEMBL 29 Released

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  We are pleased to announce the release of ChEMBL 29. This version of the database, prepared on 01/07/2021 contains: 2,703,543 compound records 2,105,464 compounds (of which 2,084,724 have mol files) 18,635,916 activities 1,383,553 assays 14,554 targets 81,544 documents Data can be downloaded from the ChEMBL FTP site:   https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29 .  Please see ChEMBL_29 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29/chembl_29_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document IDs CHEMBL4649982-CHEMBL4649998): The EUbOPEN consortium is an Innovative Medicines Initiative (IMI) funded project to enable and unlock biology in the open. The aims of the project are to assemble an open access chemogenomic library comprising about 5,000 well annotated compounds covering roughly 1,000 different proteins, to synthesiz
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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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