Large language models for Alzheimer's disease drug discovery

Area of Science:

• Biomedical Informatics
• Neuroscience
• Medicinal Chemistry

Background:

• Alzheimer's disease (AD) presents significant global health and economic challenges.
• Traditional drug discovery for AD faces limitations due to the disease's complexity and multifactorial nature.

Purpose of the Study:

• To explore the potential of large language models (LLMs) in advancing medicinal chemistry for AD drug discovery.
• To highlight how LLMs can overcome challenges in patient heterogeneity, preclinical models, and clinical trial failures.

Main Methods:

• Literature mining and synthesis of vast biomedical datasets using LLMs.
• Integration of multi-modal data for hypothesis generation, target identification, and de novo drug design.
• Assessment of LLM applications in protein structure prediction and ADME-Tox property evaluation.

Main Results:

• LLMs demonstrate capacity for enhanced hypothesis generation and target identification in AD research.
• LLMs show promise in de novo drug design and predicting drug properties (ADME-Tox).
• Case studies illustrate LLMs' role in improving efficiency and precision in AD drug discovery.

Conclusions:

• LLMs offer a paradigm shift in Alzheimer's disease research by integrating artificial intelligence and biomedicine.
• Despite challenges like data quality and interpretability, LLMs pave the way for innovative therapeutic solutions.
• This AI-driven approach fosters interdisciplinary collaboration to combat AD and improve patient outcomes.