MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

Area of Science:

• Medicinal Chemistry
• Chemical Biology
• Drug Discovery

Background:

• Traditional drug discovery is often time-consuming and requires significant chemical matter.
• Integrating advanced technologies can streamline the process of identifying and optimizing drug candidates.

Purpose of the Study:

• To develop and apply a modular technology platform for accelerated drug discovery.
• To enable rapid access to high-quality chemical matter formatted for biological assays.
• To integrate active learning models for multiparameter exploration of chemical and property space.

Main Methods:

• Utilized plate-based microscale chemistry, automated purification, and robotic liquid handling.
• Reoriented existing high-throughput assay technology for comprehensive data generation.
• Employed active learning models to drive compound optimization through multiparameter exploration.

Main Results:

• Achieved rapid access to high-quality chemical matter suitable for assays.
• Platform demonstrated efficiency and adherence to green chemistry principles by minimizing chemical consumption.
• Generated a full package of relevant data for compounds in each learning cycle, enabling knowledge generation.

Conclusions:

• The developed modular platform significantly accelerates the compound optimization process in drug discovery.
• This technology enables knowledge generation for drug discovery projects in unprecedented timeframes.
• The platform's efficiency and green chemistry approach offer a sustainable solution for pharmaceutical research.