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.

SureChEMBL: a large-scale, chemically annotated patent document database.

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/.

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure-activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets.

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.

ADME SARfari: comparative genomics of drug metabolizing systems.

MOTIVATION: ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.

The ChEMBL bioactivity database: an update.

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.

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

UNLABELLED: 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.

Activity, assay and target data curation and quality in the ChEMBL database.

The emergence of a number of publicly available bioactivity databases, such as ChEMBL, PubChem BioAssay and BindingDB, has raised awareness about the topics of data curation, quality and integrity. Here we provide an overview and discussion of the current and future approaches to activity, assay and target data curation of the ChEMBL database. This curation process involves several manual and automated steps and aims to: (1) maximise data accessibility and comparability; (2) improve data integrity and flag outliers, ambiguities and potential errors; and (3) add further curated annotations and mappings thus increasing the usefulness and accuracy of the ChEMBL data for all users and modellers in particular. Issues related to activity, assay and target data curation and integrity along with their potential impact for users of the data are discussed, alongside robust selection and filter strategies in order to avoid or minimise these, depending on the desired application.

ChEMBLSpace--a graphical explorer of the chemogenomic space covered by the ChEMBL database.

MOTIVATION: The ChEMBLSpace graphical explorer enables the identification of compounds from the ChEMBL database, which exhibit a desirable polypharmacology profile. This profile can be predefined or created iteratively, and the tool can be extended to other data sources.

Target prediction for an open access set of compounds active against Mycobacterium tuberculosis.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), infects an estimated two billion people worldwide and is the leading cause of mortality due to infectious disease. The development of new anti-TB therapeutics is required, because of the emergence of multi-drug resistance strains as well as co-infection with other pathogens, especially HIV. Recently, the pharmaceutical company GlaxoSmithKline published the results of a high-throughput screen (HTS) of their two million compound library for anti-mycobacterial phenotypes. The screen revealed 776 compounds with significant activity against the M. tuberculosis H37Rv strain, including a subset of 177 prioritized compounds with high potency and low in vitro cytotoxicity. The next major challenge is the identification of the target proteins. Here, we use a computational approach that integrates historical bioassay data, chemical properties and structural comparisons of selected compounds to propose their potential targets in M. tuberculosis. We predicted 139 target--compound links, providing a necessary basis for further studies to characterize the mode of action of these compounds. The results from our analysis, including the predicted structural models, are available to the wider scientific community in the open source mode, to encourage further development of novel TB therapeutics.

Screening and hit evaluation of a chemical library against blood-stage Plasmodium falciparum.

BACKGROUND: In view of the need to continuously feed the pipeline with new anti-malarial agents adapted to differentiated and more stringent target product profiles (e.g., new modes of action, transmission-blocking activity or long-duration chemo-protection), a chemical library consisting of more than 250,000 compounds has been evaluated in a blood-stage Plasmodium falciparum growth inhibition assay and further assessed for chemical diversity and novelty. METHODS: The selection cascade used for the triaging of hits from the chemical library started with a robust three-step in vitro assay followed by an in silico analysis of the resulting confirmed hits. Upon reaching the predefined requirements for selectivity and potency, the set of hits was subjected to computational analysis to assess chemical properties and diversity. Furthermore, known marketed anti-malarial drugs were co-clustered acting as 'signposts' in the chemical space defined by the hits. Then, in cerebro evaluation of the chemical structures was performed to identify scaffolds that currently are or have been the focus of anti-malarial medicinal chemistry programmes. Next, prioritization according to relaxed physicochemical parameters took place, along with the search for structural analogues. Ultimately, synthesis of novel chemotypes with desired properties was performed and the resulting compounds were subsequently retested in a P. falciparum growth inhibition assay. RESULTS: This screening campaign led to a 1.25% primary hit rate, which decreased to 0.77% upon confirmatory repeat screening. With the predefined potency (EC50 < 1 µM) and selectivity (SI > 10) criteria, 178 compounds progressed to the next steps where chemical diversity, physicochemical properties and novelty assessment were taken into account. This resulted in the selection of 15 distinct chemical series. CONCLUSION: A selection cascade was applied to prioritize hits resulting from the screening of a medium-sized chemical library against blood-stage P. falciparum. Emphasis was placed on chemical novelty whereby computational clustering, data mining of known anti-malarial chemotypes and the application of relaxed physicochemical filters, were key to the process. This led to the selection of 15 chemical series from which ten confirmed their activity when newly synthesized sample were tested.

The impact of breast cosmetic and functional outcomes on quality of life: long-term results from the St. George and Wollongong randomized breast boost trial.

The aims of this study were to evaluate the impact of cosmetic and functional outcomes after breast-conserving surgery (BCS) and radiation on quality of life (QOL). In this exploratory analysis; baseline, 5 and 10 years data of patient's assessment of breast cosmesis, arm swelling/pain, limitation of movement, loss of feeling in fingers and breast sensitivity/tenderness were dichotomized and their impact on QOL (QLQ-C30) were assessed. Multivariable modelling was also performed to assess associations with QOL. The St. George and Wollongong randomized trial randomized 688 patients into the boost and no boost arms. 609, 580, and 428 patients had baseline, 5 and 10 years cosmetic data available, respectively. Similar numbers had the various functional assessments in the corresponding period. By univariate analysis, cosmesis and a number of functional outcomes were highly associated with QOL. Adjusted multivariate modelling showed that cosmesis remained associated with QOL at 5 and 10 years. Breast sensitivity, arm pain, breast separation, age and any distant cancer event were also associated with QOL on multivariate modelling at 10 years. This study highlights the importance of maintaining favorable cosmetic and functional outcomes following BCS. In addition, the clinically and statistically significant relationship between functional outcomes and QOL shows the importance for clinicians and allied health professionals in identifying, discussing, managing, and limiting these effects in women with breast cancer in order to maintain QOL.

The effect of imaging modality (magnetic resonance imaging vs. computed tomography) and patient position (supine vs. prone) on target and organ at risk doses in partial breast irradiation.

INTRODUCTION: Conventionally computed tomography (CT) has been used to delineate target volumes in radiotherapy; however, magnetic resonance imaging (MRI) is being continually integrated into clinical practice; therefore, the investigation into targets derived from MRI is warranted. The purpose of this study was to evaluate the impact of imaging modality (MRI vs. CT) and patient positioning (supine vs. prone) on planning target volumes (PTVs) and organs at risk (OARs) for partial breast irradiation (PBI). METHODS: A retrospective data set, of 35 patients, was accessed where each patient had undergone MRI and CT imaging for tangential whole breast radiotherapy in both the supine and prone position. PTVs were defined from seroma cavity (SC) volumes delineated on each respective image, resulting in 4 PTVs per patient. PBI plans were generated with 6MV external beam radiotherapy (EBRT) using the TROG 06.02 protocol guidelines. A prescription of 38.5Gy in 10 fractions was used for all cases. The impact analysis of imaging modality and patient positioning included dose to PTVs, and OARs based on agreed criteria. Statistical analysis was conducted though Mann-Whitey U, Fisher's exact and chi-squared testing (P < 0.005). RESULTS: Twenty-four patients were eligible for imaging analysis. However, positioning analysis could only be investigated on 19 of these data sets. No statistically significant difference was found in OAR doses based on imaging modality. Supine patient position resulted in lower contralateral breast dose (0.10Gy ± 0.35 vs. 0.33Gy ± 0.78, p = 0.011). Prone positioning resulted in a lower dose to ipsilateral lung volumes (10.85Gy ± 11.37 vs. 3.41Gy ± 3.93, P = <0.001). CONCLUSIONS: PBI plans with PTVs derived from MRI exhibited no clinically significant differences when compared to plans created from CT in relation to plan compliance and OAR dose. Patient position requires careful consideration regardless of imaging modality chosen. Although there was no proven superiority of MRI derived target volumes, it indicates that MRI could be considered for PBI target delineation.

Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity.

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.

Optimal Piecewise Linear Regression Algorithm for QSAR Modelling.

Quantitative Structure-Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region. A regularisation term is introduced to prevent overfitting problems and implicitly selects the most informative features. As OPLRAreg is based on mathematical programming, a flexible and transparent representation for optimisation problems, the algorithm also permits customised constraints to be easily added to the model. The proposed algorithm is presented as a more interpretable alternative to other commonly used machine learning algorithms and has shown comparable predictive accuracy to Random Forest, Support Vector Machine and Random Generalised Linear Model on tests with five QSAR data sets compiled from the ChEMBL database.

The impact of imaging modality (CT vs MRI) and patient position (supine vs prone) on tangential whole breast radiation therapy planning.

PURPOSE: The purpose of this study was to evaluate the impact of magnetic resonance imaging (MRI) versus computed tomography (CT)-derived planning target volumes (PTVs), in both supine and prone positions, for whole breast (WB) radiation therapy. METHODS AND MATERIALS: Four WB radiation therapy plans were generated for 28 patients in which PTVs were generated based on CT or MRI data alone in both supine and prone positions. A 6-MV tangential intensity modulated radiation therapy technique was used, with plans designated as ideal, acceptable, or noncompliant. Dose metrics for PTVs and organs at risk were compared to analyze any differences based on imaging modality (CT vs MRI) or patient position (supine vs prone). RESULTS: With respect to imaging modality 2/11 whole breast planning target volume (WB_PTV) dose metrics (percentage of PTV receiving 90% and 110% of prescribed dose) displayed statistically significant differences; however, these differences did not alter the average plan compliance rank. With respect to patient positioning, the odds of having an ideal plan versus a noncompliant plan were higher for the supine position compared with the prone position (P = .026). The minimum distance between the seroma cavity planning target volume (SC_PTV) and the chest wall was increased with prone positioning (P < .001, supine and prone values 1.1 mm and 8.7 mm, respectively). Heart volume was greater in the supine position (P = .005). Heart doses were lower in the supine position than prone (P < .01, mean doses 3.4 ± 1.55 Gy vs 4.4 ± 1.13 Gy for supine vs prone, respectively). Mean lung doses met ideal dose constraints in both positions, but were best spared in the prone position. The contralateral breast maximum dose to 1cc (D1cc) showed significantly lower doses in the supine position (P < .001, 4.64 Gy vs 9.51 Gy). CONCLUSIONS: Planning with PTVs generated from MRI data showed no clinically significant differences from planning with PTVs generated from CT with respect to PTV and doses to organs at risk. Prone positioning within this study reduced mean lung dose and whole heart volumes but increased mean heart and contralateral breast doses compared with supine.

Unexplored therapeutic opportunities in the human genome.

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

Unexplored therapeutic opportunities in the human genome.

This corrects the article DOI: 10.1038/nrd.2018.14.

Target Identification of Mycobacterium tuberculosis Phenotypic Hits Using a Concerted Chemogenomic, Biophysical, and Structural Approach.

Mycobacterium phenotypic hits are a good reservoir for new chemotypes for the treatment of tuberculosis. However, the absence of defined molecular targets and modes of action could lead to failure in drug development. Therefore, a combination of ligand-based and structure-based chemogenomic approaches followed by biophysical and biochemical validation have been used to identify targets for Mycobacterium tuberculosis phenotypic hits. Our approach identified EthR and InhA as targets for several hits, with some showing dual activity against these proteins. From the 35 predicted EthR inhibitors, eight exhibited an IC50 below 50 µM against M. tuberculosis EthR and three were confirmed to be also simultaneously active against InhA. Further hit validation was performed using X-ray crystallography yielding eight new crystal structures of EthR inhibitors. Although the EthR inhibitors attain their activity against M. tuberculosis by hitting yet undefined targets, these results provide new lead compounds that could be further developed to be used to potentiate the effect of EthA activated pro-drugs, such as ethionamide, thus enhancing their bactericidal effect.