High-throughput synthesis provides data for predicting molecular properties and reaction success

Area of Science:

• Medicinal Chemistry
• Organic Synthesis
• Computational Chemistry

Background:

• Drug discovery requires diverse molecular scaffolds, but synthesis faces challenges in balancing diversity, accessibility, and property prediction.
• Current methods often lack the throughput and predictive power needed for efficient scaffold generation.

Purpose of the Study:

• To develop and validate a platform for high-throughput synthesis and property prediction of N-heterocycles for drug discovery.
• To leverage photocatalysis, automation, and deep learning to overcome limitations in scaffold generation.

Main Methods:

• Implemented a platform combining photocatalytic N-heterocycle synthesis with high-throughput experimentation.
• Conducted 1152 discrete reactions, followed by automated purification and physicochemical assays.
• Utilized deep learning models to predict synthesizability and analyze structure-property relationships.

Main Results:

• Successfully generated stereochemically diverse C-substituted N-saturated heterocycles.
• Developed predictive models for compound synthesizability with high accuracy.
• Identified novel structure-property relationships.

Conclusions:

• The integrated platform significantly increases throughput and confidence in preparing drug-like molecules.
• Photocatalysis, automation, and deep learning offer a powerful approach to accelerate medicinal chemistry.
• This methodology enables rational design and optimization of novel chemical entities.