Neural Network for Drug-Target Interaction Prediction
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Solution Overview
Problem
Current secure computation frameworks struggle to scale for complex computations over large datasets, particularly in drug-target interaction prediction, due to high communication costs and inability to handle side information, leading to privacy concerns and hindered collaboration in biomedical research.
Innovation Solution
A secure and scalable cryptographic pipeline using secret sharing and recent optimizations like generalized Beaver triples and shared pseudorandom number generators enables privacy-preserving collaboration by training a neural network on jointly pooled data, allowing for efficient computation and generalization to unseen data points while maintaining data confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic protocols are used to ensure data confidentiality in pharmacological collaboration, then data privacy is protected, but computation overhead and communication costs increase significantly
Solution Approach 1:
The patent segments the computation process into distinct phases: data preprocessing and encoding, secure multi-party computation execution, and result aggregation. By dividing the complex cryptographic computation into manageable segments, the system reduces overall computation overhead while maintaining data confidentiality through controlled application of cryptographic operations at each stage
Solution Approach 2:
The patent introduces trusted execution environments (TEEs) and secure enclaves as intermediary layers between the raw data and the computation process. These intermediaries handle the cryptographic operations and secure data transmission, reducing the burden on participating parties while ensuring data confidentiality without requiring each party to implement complex cryptographic protocols independently
2Reliability
If secure multi-party computation is implemented for drug-target interaction prediction, then data privacy is maintained, but the system cannot scale to large datasets with millions of interactions
Solution Approach 1:
The patent performs preliminary data processing, filtering, and aggregation before applying secure multi-party computation. By pre-processing the data to identify only the most relevant drug-target interactions and aggregating redundant information, the system reduces the data volume requiring cryptographic processing, enabling scaling to large datasets while maintaining privacy guarantees
Solution Approach 2:
The patent applies secure computation selectively to only the most critical data elements and computation steps rather than encrypting and processing all data uniformly. This partial application of cryptographic protocols to high-value data points enables the system to scale efficiently while maintaining privacy protection where it is most needed
3Reliability
If traditional secure computation frameworks are used, then data confidentiality is preserved, but communication costs become prohibitive for distributed collaboration
Solution Approach 1:
The patent merges multiple communication rounds into fewer aggregated operations by combining data from multiple participating parties into batched secure computations. This consolidation reduces the total number of communication exchanges required while maintaining confidentiality, significantly lowering communication costs for distributed collaboration
Solution Approach 2:
The patent uses cryptographic commitments and hash-based verification mechanisms that allow parties to verify data integrity and computational correctness without requiring direct communication about the underlying data. This copying and verification approach reduces communication overhead by replacing expensive data exchange with efficient cryptographic verification
Data Source
AI summary
Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).