AI-Integrated QSAR Modeling for Enhanced Drug Discovery: From Classical Approaches to Deep Learning and Structural...

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Pharmacology

Background:

• Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting drug efficacy.
• Traditional QSAR methods have limitations in handling complex molecular data.
• Artificial intelligence (AI) integration offers enhanced capabilities for drug discovery.

Purpose of the Study:

• To review the evolution of QSAR methods integrated with AI in drug discovery.
• To highlight advanced AI techniques and complementary computational tools.
• To discuss challenges and future trends in AI-driven drug development.

Main Methods:

• Review of classical QSAR (e.g., multiple linear regression, partial least squares).
• Exploration of advanced machine learning and deep learning models (e.g., graph neural networks, SMILES-based transformers).
• Integration of molecular docking and molecular dynamics simulations.
• Discussion of PROTACs, targeted protein degradation, ADMET prediction, and data platforms.

Main Results:

• AI significantly enhances the speed, accuracy, and scalability of identifying therapeutic compounds.
• Advanced AI methods provide deeper mechanistic insights into ligand-target interactions.
• PROTACs, ADMET prediction, and accessible platforms are key areas of focus.

Conclusions:

• AI-driven QSAR is revolutionizing drug discovery pipelines.
• Addressing challenges in interpretability, regulation, and ethics is vital for AI adoption.
• This review serves as a guide for implementing explainable and data-rich computational models.