Integration of Robotic Techniques for Automating Drug Discovery
Project Summary
This interdisciplinary project will feature the technology development necessary to automate aspects of drug discovery, namely testing the binding affinity of drug molecules towards biological targets. The project will involve the use of robotic arms, and simple coding, to weigh chemical materials and transport purified molecules into high-throughput bioassays. There is also scope to include sample preparation, sample purification, reaction development and machine learning to further automate this process. This project will develop many skills, as this integrates conventional biological research into areas of computer science, chemistry and more.
The automated workflow developed in this summer project will lead to improvements in bioactive molecule identification using High-Throughput Experimentation (HTE), laboratory automation and machine learning. This methodology can therefore greatly accelerate bioactive molecule identification and significantly reduce costs, enabling autonomous drug-candidate discovery for many diseases - including rare diseases and diseases of the developing world that are currently economically unfeasible to research. Establishing this automated workflow for faster drug discovery also enables protection against future diseases, either as a result of disease migration due to climate change or future global pandemics.
Training: Machine learning courses, coding, hardware integration, use of laboratory equipment for: reaction purification, reaction analysis, binding affinity, lab automation and more.