Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

2026-04-20

AI Summary

This study addresses the urgent need for effective therapeutics against Alzheimer’s disease by developing a data-driven strategy to identify bioactive natural compounds. Integrating cheminformatics and machine learning, the authors constructed a random forest classification model using molecular descriptors computed with Dragon and preprocessed via Open Babel, enabling high-throughput virtual screening of natural product libraries. The model achieved an accuracy of 0.5970 and a recall of 0.6590, successfully prioritizing 73 candidate compounds. Key molecular features contributing to anti-Alzheimer activity were identified, including atomic polarizability, bond order, and the count of non-hydrogen bonds. These findings offer novel insights and a computational framework to guide the rational design of Alzheimer’s therapeutics.

Technology Category

• Machine Learning: Active Learning
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Data Mining & Knowledge Management: Data Compression

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory, cognition, and behavior. It is characterized by the accumulation of abnormal proteins in the brain, including amyloid-beta plaques and neurofibrillary tangles of tau protein, which disrupt neuronal communication and lead to neuronal death. Early manifestations typically include mild memory impairment and reduced ability to acquire new information. As the disease progresses, patients experience severe cognitive decline, loss of independence, and significant personality and behavioral changes. Although the exact etiology of Alzheimer disease remains unclear, factors such as age, genetic predisposition, lifestyle, and cardiovascular health contribute to its development. While no definitive cure exists, early diagnosis, pharmacological interventions, and supportive care can slow progression and improve quality of life. This study presents a predictive cheminformatics-based model for identifying natural medicinal compounds with potential therapeutic efficacy against Alzheimer disease. The model functions as a drug screening system utilizing molecular descriptors and machine learning to detect anti-Alzheimer activity. More than 7,000 compounds from ChEBI, SynSysNet, and INDOFINE were preprocessed using Open Babel and analyzed with Dragon descriptors. A Random Forest classifier trained on approved treatments achieved moderate performance, with precision of 0.5970 and recall of 0.6590, identifying 73 candidate compounds. Key descriptors included atomic polarizability, bond multiplicity, and non-hydrogen bond counts.These findings demonstrate the value of cheminformatics in early-stage drug discovery for Alzheimer disease.

Problem

• Alzheimer disease
• natural medicinal compounds
• predictive modeling
• cheminformatics
• drug discovery

Innovation

• cheminformatics
• machine learning
• molecular descriptors
• Alzheimer disease
• drug screening