Deep generative molecular design and its value in modern drug discovery

Area of Science:

• Computational Chemistry
• Artificial Intelligence in Drug Discovery
• Molecular Modeling

Background:

• Deep generative models are transforming de novo drug design.
• Advances in deep learning, molecular representation, and structure-aware modeling enable novel molecule generation.
• Algorithms can now propose molecules meeting complex pharmacological constraints, speeding up hit identification.

Purpose of the Study:

• To review recent advances in generative molecular design.
• To outline neural network-based frameworks, reinforcement learning, diffusion models, and transformers for molecular generation.
• To analyze practical applications, bottlenecks, and emerging solutions in generative AI for drug discovery.

Main Methods:

• Systematic literature search in Google Scholar and PubMed (up to December 2025).
• Review of neural network-based frameworks, reinforcement learning, diffusion models, and transformers.
• Analysis of generative AI's role in molecule generation and optimization.

Main Results:

• Generative AI enables the creation of novel, property-optimized molecules beyond traditional libraries.
• Various AI frameworks can generate and optimize molecular structures effectively.
• Practical applications demonstrate translational progress in drug discovery.

Conclusions:

• Generative AI promises better hypotheses and synthetically accessible, biologically plausible molecules optimized for potency and pharmacokinetics.
• Future advancements will involve multimodal foundation models integrating chemistry, protein structure, and cellular response.
• Integrated generative molecular design will guide lead optimization and reshape drug discovery and development.