An open source chemical structure curation pipeline using RDKit

BACKGROUND

The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to consistent rules. In order to maintain the quality of the final database and to easily compare and integrate data on the same compound from different sources it is necessary for the chemical structures in the database to be appropriately standardised.

RESULTS

A chemical curation pipeline has been developed using the open source toolkit RDKit. It comprises three components: a Checker to test the validity of chemical structures and flag any serious errors; a Standardizer which formats compounds according to defined rules and conventions and a GetParent component that removes any salts and solvents from the compound to create its parent. This pipeline has been applied to the latest version of the ChEMBL database as well as uncurated datasets from other sources to test the robustness of the process and to identify common issues in database molecular structures.

CONCLUSION

All the components of the structure pipeline have been made freely available for other researchers to use and adapt for their own use. The code is available in a GitHub repository and it can also be accessed via the ChEMBL Beaker webservices. It has been used successfully to standardise the nearly 2 million compounds in the ChEMBL database and the compound validity checker has been used to identify compounds with the most serious issues so that they can be prioritised for manual curation.

ChEMBL: towards direct deposition of bioassay data

ChEMBL is a large, open-access bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012, 2014 and 2017 Nucleic Acids Research Database Issues. In the last two years, several important improvements have been made to the database and are described here. These include more robust capture and representation of assay details; a new data deposition system, allowing updating of data sets and deposition of supplementary data; and a completely redesigned web interface, with enhanced search and filtering capabilities.

Reply to "Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery"

In response to Krstajic's letter to the editor concerning our published paper, we here take the opportunity to reply, to re-iterate that no errors in our work were identified, to provide further details, and to re-emphasise the outputs of our study. Moreover, we highlight that all of the data are freely available for the wider scientific community (including the aforementioned correspondent) to undertake follow-on studies and comparisons.

Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery

Structure–activity relationship modelling is frequently used in the early stage of drug discovery to assess the activity of a compound on one or several targets, and can also be used to assess the interaction of compounds with liability targets. QSAR models have been used for these and related applications over many years, with good success. Conformal prediction is a relatively new QSAR approach that provides information on the certainty of a prediction, and so helps in decision-making. However, it is not always clear how best to make use of this additional information. In this article, we describe a case study that directly compares conformal prediction with traditional QSAR methods for large-scale predictions of target-ligand binding. The ChEMBL database was used to extract a data set comprising data from 550 human protein targets with different bioactivity profiles. For each target, a QSAR model and a conformal predictor were trained and their results compared. The models were then evaluated on new data published since the original models were built to simulate a “real world” application. The comparative study highlights the similarities between the two techniques but also some differences that it is important to bear in mind when the methods are used in practical drug discovery applications.

A Comparative Analysis of Drug-Induced Hepatotoxicity in Clinically Relevant Situations

Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application.

Liver toxicity may occur at drug levels above the therapeutic range and is thus a crucial problem in clinical care. However, the cellular changes induced by drug administration of therapeutic and toxic doses in humans are still not well understood. We here coupled patient-specific drug concentration-time profiles following oral administration of therapeutic and toxic doses with in vitro drug response data to predict toxic changes that quantitatively reflect the transition from desired drug effects to undesired toxic reactions. These toxic changes were comparatively evaluated for fifteen hepatotoxic drugs to identify subsets of drugs, which show similar drug effects on key cellular processes, functional classes of genes, and individual genes, respectively. In addition, analyzing toxic changes for individual genes allowed the prediction of molecular biomarkers and potential drug-drug interactions. Our results may hence support the early diagnosis of liver toxicity in clinical care in the future and may, moreover, help to assess potential risks of drug combination therapies.

The ChEMBL database in 2017

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since then, alongside the continued extraction of data from the medicinal chemistry literature, new sources of bioactivity data have also been added to the database. These include: deposited data sets from neglected disease screening; crop protection data; drug metabolism and disposition data and bioactivity data from patents. A number of improvements and new features have also been incorporated. These include the annotation of assays and targets using ontologies, the inclusion of targets and indications for clinical candidates, addition of metabolic pathways for drugs and calculation of structural alerts. The ChEMBL data can be accessed via a web-interface, RDF distribution, data downloads and RESTful web-services.

ChEMBL web services: streamlining access to drug discovery data and utilities

ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology.

ChEMBL web services: streamlining access to drug discovery data and utilities

ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology.

myChEMBL: a virtual machine implementation of open data and cheminformatics tools

Summary: myChEMBL is a completely open platform, which combines public domain bioactivity data with open source database and cheminformatics technologies. myChEMBL consists of a Linux (Ubuntu) Virtual Machine featuring a PostgreSQL schema with the latest version of the ChEMBL database, as well as the latest RDKit cheminformatics libraries. In addition, a self-contained web interface is available, which can be modified and improved according to user specifications.

Availability and implementation: The VM is available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/VM/myChEMBL/current. The web interface and web services code is available at: https://github.com/rochoa85/myChEMBL.

Contact:jpo@ebi.ac.uk

'Be Clear on Cancer': the impact of the UK National Bowel Cancer Awareness Campaign

AIM

The National Bowel Cancer Awareness Campaign ('Be Clear on Cancer') was launched by the UK government in January 2012, encouraging people with bowel symptoms to present to primary care. Our aim was to evaluate the impact of the campaign on colorectal services in secondary care.

METHOD

Suspected cancer 2-week-wait (2WW) patients 3 months before and 3 months after the launch of the campaign were included. Demographics, reason for referral, investigations performed, cost analysis and eventual diagnoses were collected.

RESULTS

Three hundred and forty-three patients [median age 70 (36-100) years, 194 (57%) women] were seen and investigated in the 3 months prior to the launch of the campaign at an average cost of £575 per patient. Twenty-seven (8%) were diagnosed with lower gastrointestinal cancer and 29 (8%) with polyps. In the 3 months following the launch, 544 patients [median age 68 (30-92) years, 290 (53%) women] were reviewed (59% increase; P = 0.004). The 'did not attend' rate fell from 10% to 1%. Thirty-two (6%) patients were diagnosed with a lower gastrointestinal cancer and 20 (4%) with colorectal polyps. The cost per colorectal cancer detected rose from £7585.58 before the campaign to £9662.72 after launch (P = 0.04).

CONCLUSION

The 'Be Clear on Cancer' campaign has substantially increased the number of referrals under the 2WW rule, but mainly in the worried well. This has increased demands on both resources (59% more tests) and finance. Cost per cancer detected rose by 27% with no increase in funding to support the increased activity.