Accelerating drug discovery by crowdsourcing confidential data

October 18, 2018

Leveraging modern cryptographic and machine learning tools, researchers seeking to accelerate drug discovery have developed a way for multiple pharmaceutical companies and laboratories to collaborate without revealing confidential data. According to the related report, the shared experimental datasets improve the ability of predictive models designed to identify drug-target interactions (DTI), to predict new therapeutic candidates. Using this approach, drug candidates could be identified at a rate and scale far greater than current state-of-the-art methods allow, the authors say. Developing a new drug takes years of research and a large amount of resources. To address this, pharmaceutical companies sometimes collaborate, sharing knowledge and resources. While such efforts have been shown to be fruitful in some cases, they are often limited in scope due to concerns about intellectual property and competing financial interests. What's more, the sharing of data between multiple entities is restricted by a need to maintain confidentiality. Secure multiparty computation (MPC) protocols offer a modern cryptographic solution for facilitating collaboration while ensuring data privacy. However, existing MPC frameworks lack the ability to perform complex algorithms over the large datasets required to predict new therapeutic drugs. To address this need, Brian Hie and colleagues developed a computational protocol for collaborative DTI prediction based on secure MPC, which blinds sensitive data and divides computational tasks across collaborating groups. Together, the secured combined dataset - comprising millions of chemical compounds and drug target interactions - was used to train a neural network model for DTI prediction. This represents one of the first such demonstrations of secure neural networks training on large-scale real-world data, the authors say. According to their results, the protocol allowed the model to produce accurate results in under four days of training. While the method demonstrates a promising solution for pharmaceutical collaboration, the authors suggest it could also be used in other areas hindered by a lack of collaboration due to privacy concerns.

American Association for the Advancement of Science

Related Data Articles from Brightsurf:

Keep the data coming
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Astronomers are bulging with data
For the first time, over 250 million stars in our galaxy's bulge have been surveyed in near-ultraviolet, optical, and near-infrared light, opening the door for astronomers to reexamine key questions about the Milky Way's formation and history.

Novel method for measuring spatial dependencies turns less data into more data
Researcher makes 'little data' act big through, the application of mathematical techniques normally used for time-series, to spatial processes.

Ups and downs in COVID-19 data may be caused by data reporting practices
As data accumulates on COVID-19 cases and deaths, researchers have observed patterns of peaks and valleys that repeat on a near-weekly basis.

Data centers use less energy than you think
Using the most detailed model to date of global data center energy use, researchers found that massive efficiency gains by data centers have kept energy use roughly flat over the past decade.

Storing data in music
Researchers at ETH Zurich have developed a technique for embedding data in music and transmitting it to a smartphone.

Life data economics: calling for new models to assess the value of human data
After the collapse of the blockchain bubble a number of research organisations are developing platforms to enable individual ownership of life data and establish the data valuation and pricing models.

Geoscience data group urges all scientific disciplines to make data open and accessible
Institutions, science funders, data repositories, publishers, researchers and scientific societies from all scientific disciplines must work together to ensure all scientific data are easy to find, access and use, according to a new commentary in Nature by members of the Enabling FAIR Data Steering Committee.

Democratizing data science
MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Getting the most out of atmospheric data analysis
An international team including researchers from Kanazawa University used a new approach to analyze an atmospheric data set spanning 18 years for the investigation of new-particle formation.

Read More: Data News and Data Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to