Entropy-Guided Reverse-Causal AI to Identify Upstream Bottleneck Genes for Alzheimer's Drug Discovery

2026-10-06

AI Summary

This study addresses the challenge of identifying upstream regulatory genes that bridge disease mechanisms and therapeutic interventions in Alzheimer’s disease drug development by proposing an entropy-guided reverse causality framework. This framework establishes a bidirectional discovery architecture that integrates a large language model-assisted knowledge graph, network reverse tracing, and an entropy-based prioritization algorithm. By innovatively coupling upstream bottleneck identification with iterative therapeutic selection, it enables systematic tracing from pharmacological starting points to shared candidate genes. Experimental validation through the tracing of three drugs identified 46 pathways and nine key genes, pinpointing EGFR as the primary target. These results demonstrate the feasibility of localizing shared molecular targets from a drug-centric perspective.

Abstract

Identifying upstream regulators that connect several disease processes to therapeutic interventions is a central objective in Alzheimer's disease drug discovery. We propose an entropy-guided reverse-causal framework that makes candidate bottleneck genes the organizing link between disease mechanisms, pathways, molecular targets and drugs. The methodology integrates five stages: an Alzheimer's-specific knowledge graph with language-model assistance and expert review; reverse tracing from drugs to candidate genes; entropy-guided prioritization; forward propagation to drugs and complementary combinations; and staged validation with evidence feedback. The novelty lies in integrating upstream bottleneck identification, entropy-guided prioritization and iterative therapeutic selection within a dynamic, bidirectional discovery architecture. We demonstrate its molecular tracing and gene-prioritization components in a computational feasibility study using DeepDrug2 and MSigDB pathway annotations. Tracing amlodipine, indapamide and atorvastatin through a network of 11,300 molecular and drug nodes identifies 46 routes to nine genes. EGFR is the leading candidate, supported by 26 routes from all three drugs; MME and MAF rank next. These results show how pharmacological starting points can identify shared candidate genes with defined molecular connections. The framework's scientific significance lies in connecting convergent disease mechanisms to systematic intervention selection, with preservation of cognition and independence as the translational objective.

Problem

• Alzheimer's disease
• drug discovery
• upstream bottleneck genes
• therapeutic interventions

Innovation

• Entropy-Guided Prioritization
• Reverse-Causal Framework
• Knowledge Graph
• Bottleneck Genes
• Alzheimer's Disease