ArticleMolecular informatics2012
Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration.
Article in Molecular informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and pitfalls of social media research in rare genetic diseases: a systematic review.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Pooled it
- Exploring patient and family involvement in the lifecycle of an orphan drug: a scoping review.Orphanet journal of rare diseases · 2017Article
- Making Transporter Models for Drug-Drug Interaction Prediction Mobile.Drug metabolism and disposition: the biological fate of chemicals · 2015Article
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- App-etite for change.Journal of computer-aided molecular design · 2015Article
- Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data.Journal of cheminformatics · 2015Article
- Machine learning models identify molecules active against the Ebola virusF1000Research · 2015Article
- Bigger data, collaborative tools and the future of predictive drug discovery.Journal of computer-aided molecular design · 2014Article
- Ten simple rules of live tweeting at scientific conferences.PLoS computational biology · 2014Article
- Finding new collaboration models for enabling neglected tropical disease drug discovery.PLoS neglected tropical diseases · 2014Article
- Computational models for neglected diseases: gaps and opportunities.Pharmaceutical research · 2014Article
- Collaboration for rare disease drug discovery research.F1000Research · 2014Article
- New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.Journal of cheminformatics · 2014Article
- Open source drug discovery - a limited tutorial.Parasitology · 2014Review
- Why open drug discovery needs four simple rules for licensing data and models.PLoS computational biology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Open Drug Discovery Teams (ODDT) project provides a mobile app primarily intended as a research topic aggregator of predominantly open science data collected from various sources on the internet. It exists to facilitate interdisciplinary teamwork and to relieve the user from data overload, delivering access to information that is highly relevant and focused on their topic areas of interest. Research topics include areas of chemistry and adjacent molecule-oriented biomedical sciences, with an emphasis on those which are most amenable to open research at present. These include rare and neglected diseases, and precompetitive and public-good initiatives such as green chemistry. The ODDT project uses a free mobile app as user entry point. The app has a magazine-like interface, and server-side infrastructure for hosting chemistry-related data as well as value added services. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Much of the content is derived from public sources, but the platform is also amenable to commercial data input. The technology could also be readily used in-house by organizations as a research aggregator that could integrate internal and external science and discussion. The infrastructure for the app is currently based upon the Twitter API as a useful proof of concept for a real time source of publicly generated content. This could be extended further by accessing other APIs providing news and data feeds of relevance to a particular area of interest. As the project evolves, social networking features will be developed for organizing participants into teams, with various forms of communication and content management possible.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.