The vast amounts of data generated during drug discovery are not always used optimally, meaning scientists miss out on opportunities to make new discoveries using old data. One challenge lies in the fact that much of this data is owned by companies, and making it readily available to others would affect their competitiveness.
The MELLODDY project aims to establish a machine learning platform that would make it possible to learn from multiple sets of proprietary data while respecting their highly confidential nature, as data and asset owners will retain control of their information throughout the project.
Through this innovative, blockchain-based solution, the pharmaceutical companies in the project aim to demonstrate the feasibility of this approach with an unprecedented volume of competitive data in the form of over a billion drug-development-relevant data points, and hundreds of terabytes of image data that annotate the biological effects of more than 10 million small molecules. The platform would also take a federated machine learning approach, meaning that the learning effort is not centralised but spread over different, physically separated partners.
The hope is that this solution will deliver insights that will advance drug development by making it easier to identify which small molecules show the most promise for further research.