Artificial intelligence in molecular de novo design: Integration with experiment.

Area of Science:

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

Background:

• Molecular de novo design aims to create novel molecules with desired properties.
• Traditional methods are often time-consuming and resource-intensive.
• Artificial intelligence (AI), particularly deep learning, offers new paradigms for molecular design.

Purpose of the Study:

• To review the latest advancements in applying AI, specifically deep learning, to molecular de novo design.
• To highlight the integration of AI-driven design with experimental validation.
• To explore the connection between AI-based design, quantitative structure-activity relationship (QSAR) models, and chemistry automation.

Main Methods:

• Review of recent literature on deep learning architectures for generative molecular design.
• Analysis of studies integrating AI-generated molecules with experimental validation.
• Examination of QSAR model validation within AI-driven design workflows.
• Assessment of the emerging links between AI molecular design and automated chemistry platforms.

Main Results:

• Significant progress has been made in developing novel generative algorithms for molecular design.
• Experimental validation of AI-designed molecules is increasingly reported, serving as proof-of-principle.
• AI-based approaches are beginning to integrate with automated experimental systems and QSAR modeling.

Conclusions:

• AI, powered by deep learning, shows great potential in accelerating molecular de novo design.
• Current experimental validations confirm the feasibility of AI-guided molecular discovery.
• The field is rapidly evolving, with early-stage integration into automated chemistry workflows indicating a promising future direction.