Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework

Area of Science:

• Computational chemistry and pharmacology
• Artificial intelligence in drug discovery
• Oncology therapeutics

Background:

• Single-target therapies face limitations in treating complex diseases due to biological redundancy and resistance.
• Multi-target drug design offers a promising strategy to overcome these challenges.
• Deep generative models provide a powerful AI-driven approach for designing novel therapeutics.

Purpose of the Study:

• To provide a comprehensive overview of AI-driven deep generative modeling in multi-target drug discovery.
• To highlight recent advancements in model architectures, molecular representations, and optimization strategies.
• To discuss emerging trends and challenges in autonomous drug discovery pipelines.

Main Methods:

• Review of recent literature on AI-driven deep generative models for drug discovery.
• Analysis of breakthroughs in model architectures and molecular representations.
• Examination of goal-directed optimization and self-improving learning systems.

Main Results:

• Deep generative models offer scalable platforms for *de novo* generation and optimization of multi-target small molecules.
• Self-improving learning systems represent a transformative approach to adaptive drug design.
• Significant progress has been made in AI model architectures and optimization strategies.

Conclusions:

• AI-powered deep generative modeling is a key enabler for next-generation multi-target drug discovery.
• Addressing current challenges is crucial for advancing intelligent and autonomous drug discovery pipelines.
• The field is evolving towards more adaptive and efficient therapeutic development for complex diseases.