MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets while keeping the data private, which could enable broader pooling of sensitive pharmacological data for predictive drug discovery. Image: Hie, Cho, Berger
Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data. MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery. Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates. In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models. Due to intellectual property matters and other privacy concerns, however, these datasets remain limited in scope.
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