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How to Create and Use Plug-ins

Overview

Flexibility and responsiveness are core tenets of BARD. One way the system delivers this is through an open architecture where users can create shareable plug-ins that meet their needs. That means that computational biologists, informatics experts, developers of advanced computational methods and other adept scientists can contribute code to BARD that benefits themselves and the entire research community.

Existing and Future Plugins

Already there are three plug-ins live on BARD providing enhanced insights into research data. "Badapple" performs scaffold-based promiscuity analysis of compounds, while “SmartCyp” and “WhichCyp” perform metabolism and binding predictions of cytochrome P450. These are only the beginning. Several more plug-ins are in development and planning stages:

Name Lead Author[s] Institution Brief Description Status
Badapple Jeremy YangUNMEvidence-based promiscuity scoresreleased Oct 2012
HScaf Jeremy YangUNMHierarchical scaffold analysisIn development
SmartCyp Rajarshi GuhaNCATSPrediction of which sites in a molecule are most liable to metabolism by Cytochrome P450released March 2013
WhichCyp Rajarshi GuhaNCATSPrediction of which Cytochrome P450 isoform(s) is(are) likely to bind a drug-like moleculereleased June 2013
ProtClass Rajarshi GuhaNCATSProtein classifications (Panther) based on Uniprot IDsreleased October 2013
kNNBioactivity Oleg UrsuUNMkNN machine learning, bioactivity profile predictionreleased October 2013
QED Oleg UrsuUNMQuantitative Estimate of Drug-likeness (Molecular Property & Filtering Suite)released January 2014
Ro5 Jeremy YangUNMLipinski Rule of 5 (Molecular Property & Filtering Suite)In development
SMARTS Jeremy YangUNMSMARTS structural alerts (Molecular Property & Filtering Suite)In development
Metaprint2D Lars CarlssonAstraZenecaMetabolic site prediction based on historic metabolic dataIn development
ALOGPS Iurii Sushko, Igor TetkoHZM / VCCLAB / eADMET LogP & solubility predictionPlanned
TBE (QSAR) Diane Pozefsky, Alex TropshaUNCQSAR modelingPlanned
TBE (assay-based similarity) Vlado DancikBroadCompound similarity based on bioactivityPlanned
TBE (various predictive models) Jens Meiler, Edward LoweVanderbiltBioactivity and potency prediction using BCL-based algorithmsPlanned
TBE (various predictive models) Alexey Zakharov, Marc NicklausNCIBioactivity, toxicity, property predictionPlanned

Creating Your Own Plug-ins

Think of a computational method that you’ve developed and published. Maybe you’ve received emails from other computational experts interested in using it, but adoption is limited to those who have the expertise. Now imagine if the general research community could easily leverage your work on a rich data set without needing expert computational knowledge. With BARD, chances are you could develop a plug-in that accomplishes exactly that. So long as your code implements a small number of methods based on a defined set of signatures then it can be integrated into BARD:

How plug-ins fit into the Bard architecture

Note that while a plug-in may be developed on any computer, currently all plug-ins run on BARD’s backend server, operating in a servlet container. Our engineering support team can help you to install a copy of the BARD data warehouse and REST API on your machine for plug-in development purposes. That way your plug-in may include direct references into the BARD data warehouse, and can thus potentially offer performance roughly equivalent to our internally developed software even if extensive database queries are required. We also can support asynchronous links, allowing plug-in developers to perform calculations off-line but then return those results to their plug-in.

For more technical details on how to develop plug-ins, please see https://github.com/ncats/bard/wiki/Plugins. To initiate a request for a copy of the BARD date warehouse, please email Contact us .