Neural Network Molecular Fingerprint Encryption for Drug Discovery
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Solution Overview
Problem
Current pharmaceutical drug discovery processes face challenges in efficiently discriminating between distinct molecules with identical or similar molecular fingerprints, leading to potential harmful side effects and high development costs, while existing cryptographic techniques for sharing molecular information suffer from network delays and privacy concerns.
Innovation Solution
A system utilizing neural networks to map molecular structures into multi-dimensional fingerprints, encrypting them with symmetric homomorphic encryption, and enabling secure multi-party computation to facilitate inter-party communication, allowing for secure sharing and comparison of molecular information without revealing sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional molecular fingerprinting methods are used to map molecular structures, then computational complexity is reduced, but distinct molecules with identical structures cannot be discriminated
Solution Approach 1:
The patent transitions from traditional one-dimensional molecular fingerprints to multi-dimensional molecular embeddings generated by neural networks. This dimensional expansion allows the system to capture nuanced molecular characteristics and distinguish between molecules that have identical traditional fingerprints, thereby resolving the contradiction between computational simplicity and discrimination accuracy.
2Loss of information
If secure multiparty computation (MPC) is used to share sensitive molecular information, then privacy is protected, but significant network delays occur
Solution Approach 1:
The patent extracts only the essential molecular features into compact neural network embeddings before sharing, rather than exchanging complete molecular structures or using heavy MPC protocols. This extraction approach maintains privacy protection while significantly reducing communication overhead and network delays.
Solution Approach 2:
The system uses neural network embeddings as simplified copies or representations of the original molecular structures. These embeddings capture the essential information needed for comparison and analysis while being much more compact and efficient to transmit, avoiding the need for complex secure multiparty computation on full molecular data.
3Productivity
If databases of toxic molecules are used to discard unsuitable drugs, then resources are saved, but harmful side effects are still detected at later stages causing billion-dollar losses
Solution Approach 1:
The patent enables pharmaceutical companies to perform preliminary toxicity screening by comparing molecular embeddings against shared toxic molecule databases before investing heavily in later development stages. This advance detection using neural network-based molecular representations allows for more accurate toxicity prediction, discarding problematic molecules earlier and preventing billion-dollar losses from late-stage failures.
Data Source
AI summary
Disclosed is a system for processing molecular information and a method of facilitating inter-party communication relating to molecular fingerprints. The system comprises a server arrangement configured to receive an input of the molecular information, wherein the molecular information comprises information pertaining to molecular structure of at least one molecule: process the molecular information to map the molecular structure of each of the at least one molecule in the input to a molecular fingerprint corresponding thereto using neural networks. wherein the molecular fingerprint is a representation of the at least one molecule in a multi-dimensional space that enables comparison of the at least one molecule with other molecules: encrypt the molecular fingerprints using a symmetric encryption algorithm; and store the encrypted molecular fingerprints in a data repository.