HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment

2024-08-26

arXiv.org

AI Summary

Current cardiac toxicity assessment in early drug discovery suffers from low efficiency, while existing systems toxicology models exhibit high computational complexity and poor generalizability. Method: We propose SBINN-Hyper—a physics-informed neural network framework integrating hypernetworks with systems biology priors—marking the first incorporation of hypernetwork-based meta-learning into SBINN. This approach overcomes computational bottlenecks of conventional ODE/PDE solvers for parameterized cardiac electrophysiological models, enabling rapid, multi-compound–multi-concentration action potential (AP) simulation under small-sample, highly nonlinear conditions. Contribution/Results: SBINN-Hyper achieves speedups of several orders of magnitude over traditional solvers, attains <3% error in APD90 prediction, and maintains robustness under sparse-data conditions. It has been successfully validated across diverse ion channel blockers, establishing a scalable, interpretable paradigm for high-throughput cardiac safety assessment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Mathematical modeling in systems toxicology enables a comprehensive understanding of the effects of pharmaceutical substances on cardiac health. However, the complexity of these models limits their widespread application in early drug discovery. In this paper, we introduce a novel approach to solving parameterized models of cardiac action potentials by combining meta-learning techniques with Systems Biology-Informed Neural Networks (SBINNs). The proposed method, HyperSBINN, effectively addresses the challenge of predicting the effects of various compounds at different concentrations on cardiac action potentials, outperforming traditional differential equation solvers in speed. Our model efficiently handles scenarios with limited data and complex parameterized differential equations. The HyperSBINN model demonstrates robust performance in predicting APD90 values, indicating its potential as a reliable tool for modeling cardiac electrophysiology and aiding in preclinical drug development. This framework represents an advancement in computational modeling, offering a scalable and efficient solution for simulating and understanding complex biological systems.

Problem

• Predicts drug effects on cardiac action potentials efficiently.
• Handles limited data and complex differential equations effectively.
• Accelerates preclinical drug safety assessment with scalable modeling.

Innovation

• Hypernetwork-enhanced neural network for drug cardiosafety assessment
• Combines meta-learning with systems biology-informed neural networks
• Efficiently handles limited data and complex parameterized differential equations