Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The Open Targets Platform ( https://platform.opentargets.org) is a unique, open-source, publicly-available knowledge base providing data and tooling for systematic drug target identification, annotation, and prioritisation. Since our last report, we have expanded the scope of the Platform through a number of significant enhancements and data updates, with the aim to enable our users to formulate more flexible and impactful therapeutic hypotheses. In this context, we have completely revamped our target–disease associations page with more interactive facets and built-in functionalities to empower users with additional control over their experience using the Platform, and added a new Target Prioritisation view. This enables users to prioritise targets based upon clinical precedence, tractability, doability and safety attributes. We have also implemented a direction of effect assessment for eight sources of target–disease association evidence, showing the effect of genetic variation on the function of a target is associated with risk or protection for a trait to inform on potential mechanisms of modulation suitable for disease treatment. These enhancements and the introduction of new back and front-end technologies to support them have increased the impact and usability of our resource within the drug discovery community.

Graphical Abstract

Related collections

Most cited references 60

Record: found
Abstract: found
Article: found

Is Open Access

The mutational constraint spectrum quantified from variation in 141,456 humans

Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao … (2021)

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

0 comments Cited 4027 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2017)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

0 comments Cited 3822 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Attention Is All You Need

Ashish Vaswani, Noam Shazeer, Niki Parmar … (2017)

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures

0 comments Cited 3348 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Annalisa Buniello:

ORCID: https://orcid.org/0000-0002-4623-8642

Daniel Suveges

Carlos Cruz-Castillo

Manuel Bernal Llinares:

ORCID: https://orcid.org/0000-0002-7368-180X

Helena Cornu

Irene Lopez

Kirill Tsukanov

Juan María Roldán-Romero

Chintan Mehta

Luca Fumis

Graham McNeill

James D Hayhurst

Ricardo Esteban Martinez Osorio

Ehsan Barkhordari

Javier Ferrer

Miguel Carmona

Prashant Uniyal

Maria J Falaguera

Polina Rusina

Ines Smit

Jeremy Schwartzentruber

Tobi Alegbe

Vivien W Ho

Daniel Considine

Xiangyu Ge

Szymon Szyszkowski

Yakov Tsepilov

Maya Ghoussaini

Ian Dunham:

ORCID: https://orcid.org/0000-0003-2525-5598

David G Hulcoop

Ellen M McDonagh:

ORCID: https://orcid.org/0000-0001-5806-6174

David Ochoa:

ORCID: https://orcid.org/0000-0003-1857-278X

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date Collection: 06 January 2025

Publication date (Electronic): 06 December 2024

Publication date PMC-release: 06 December 2024

Volume: 53

Issue: D1

Pages: D1467-D1475