Most cited article - PubMed ID 28753599
Probes &Drugs portal: an interactive, open data resource for chemical biology
Morphological profiling with the Cell Painting assay has emerged as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound bioactivity. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive compounds. The data were generated across four imaging sites with high-throughput confocal microscopes using the Hep G2 as well as the U2 OS cell lines. We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles validates the robustness of the generated data. We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we demonstrate the dataset's potential for facilitating more extensive exploration of MOAs.
- Keywords
- Chemistry,
- Publication type
- Journal Article MeSH
The European Chemical Biology Database (ECBD, https://ecbd.eu) serves as the central repository for data generated by the EU-OPENSCREEN research infrastructure consortium. It is developed according to FAIR principles, which emphasize findability, accessibility, interoperability and reusability of data. This data is made available to the scientific community following open access principles. The ECBD stores both positive and negative results from the entire chemical biology project pipeline, including data from primary or counter-screening assays. The assays utilize a defined and diverse library of over 107 000 compounds, the annotations of which are continuously enriched by external user supported screening projects and by internal EU-OPENSCREEN bioprofiling efforts. These compounds were screened in 89 currently deposited datasets (assays), with 48 already being publicly accessible, while the remaining will be published after a publication embargo period of up to 3 years. Together these datasets encompass ∼4.3 million experimental data points. All public data within ECBD can be accessed through its user interface, API or by database dump under the CC-BY 4.0 license.
In the last decade, zebrafish have accompanied the mouse as a robust animal model for cancer research. The possibility of screening small-molecule inhibitors in a large number of zebrafish embryos makes this model particularly valuable. However, the dynamic visualization of fluorescently labeled tumor cells needs to be complemented by a more sensitive, easy, and rapid mode for evaluating tumor growth in vivo to enable high-throughput screening of clinically relevant drugs. In this study we proposed and validated a pre-clinical screening model for drug discovery by utilizing bioluminescence as our readout for the determination of transplanted cancer cell growth and inhibition in zebrafish embryos. For this purpose, we used NanoLuc luciferase, which ensured rapid cancer cell growth quantification in vivo with high sensitivity and low background when compared to conventional fluorescence measurements. This allowed us large-scale evaluation of in vivo drug responses of 180 kinase inhibitors in zebrafish. Our bioluminescent screening platform could facilitate identification of new small-molecules for targeted cancer therapy as well as for drug repurposing.
- Keywords
- bioluminescence, cancer, high-throughput screening, inhibitor, xenotransplantation, zebrafish,
- Publication type
- Journal Article MeSH
Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.
- Keywords
- De novo drug design, Deep learning, Graphical user interface, Molecule generation, Web application,
- Publication type
- Journal Article MeSH
In 2005, the NIH Molecular Libraries Program (MLP) undertook the identification of tool compounds to expand biological insights, now termed small-molecule chemical probes. This inspired other organisations to initiate similar efforts from 2010 onwards. As a central focus of the Probes & Drugs portal (P&D), we have standardised, integrated and compared sets of declared probe compounds harvested from 12 different sources. This turned out to be challenging and revealed unexpected anomalies. Results in this work address key questions including; a) individual and total structure counts, b) overlaps between sources, c) comparisons with selected PubChem sources and d) investigating the probe coverage of druggable targets. In addition, we developed new high-level scoring schemes to filter collections down to probes of higher quality. This generated 548 high-quality chemical probes (HQCP) covering 447 distinct protein targets. This HQCP collection has been added to the P&D portal and will be regularly updated as established sources expand and new ones release data.
- Publication type
- Journal Article MeSH
